librarian-bots/card_with_first_commit · Datasets at Hugging Face

modelId stringlengths 4 81	tags sequence	pipeline_tag stringclasses 17 values	config dict	downloads int64 0 59.7M	first_commit unknown	card stringlengths 51 438k
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	16,451	null	https://www.nace.org/network/members/profile?UserKey=461a690a-bff6-4e4c-be63-ea8e39264459 https://www.nace.org/network/members/profile?UserKey=b4a6a66a-fb8a-4f2b-8af9-04f003ad9d46 https://www.nace.org/network/members/profile?UserKey=24544ab2-551d-42aa-adbe-7a1c1d68fd9c https://www.nace.org/network/members/profile?UserKey=3e8035d5-056a-482d-9010-9883e5990f4a https://www.nace.org/network/members/profile?UserKey=d7241c69-28c4-4146-a077-a00cc2c9ccf5 https://www.nace.org/network/members/profile?UserKey=2c58c2fb-13a4-4e5a-b044-f467bb295d83 https://www.nace.org/network/members/profile?UserKey=dd8a290c-e53a-4b56-9a17-d35dbcb6b8bd https://www.nace.org/network/members/profile?UserKey=0e96a1af-91f4-496a-af02-6d753a1bbded
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	18	null	https://ragbrai.com/groups/hd-movie-watch-french-exit-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-nobody-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-voyagers-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-godzilla-vs-kong-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-raya-and-the-last-dragon-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-mortal-kombat-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-the-father-2021-full-movie-online-for-free/
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	71	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer intermediate model (B6-6-6 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `intermediate` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/intermediate-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/intermediate-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	73	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer intermediate model (B6-6-6 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate") model = FunneModel.from_pretrained("funnel-transformer/intermediate") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate") model = TFFunnelModel.from_pretrained("funnel-transformer/intermediatesmall") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-ca	[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	580	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer large model (B8-8-8 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `large` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/large-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/large-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-ner	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	42	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer large model (B8-8-8 with decoder) Pretrained model on English language using a similar objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large") model = FunneModel.from_pretrained("funnel-transformer/large") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large") model = TFFunnelModel.from_pretrained("funnel-transformer/large") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:1905.05700", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	37	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer medium model (B6-3x2-3x2 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `medium` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/medium-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/medium-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	32	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer medium model (B6-3x2-3x2 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium") model = FunneModel.from_pretrained("funnel-transformer/medium") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium") model = TFFunnelModel.from_pretrained("funnel-transformer/medium") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	54	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer small model (B4-4-4 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `small` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/small-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/small-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	27	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer small model (B4-4-4 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small") model = FunneModel.from_pretrained("funnel-transformer/small") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small") model = TFFunnelModel.from_pretrained("funnel-transformer/small") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "has_space" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	19,850	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer xlarge model (B10-10-10 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Note: This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `xlarge` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/xlarge-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/xlarge-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-da	[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	449	null	--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer xlarge model (B10-10-10 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge") model = FunneModel.from_pretrained("funnel-transformer/xlarge") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge") model = TFFunnelModel.from_pretrained("funnel-transformer/xlarge") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	62	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-finetuned-bbc-headline results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-bbc-headline This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 167 \| 2.2978 \| 31.8313 \| 10.3824 \| 29.6182 \| 29.4336 \| 10.3153 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	132	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-finetuned-bbc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-bbc This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 334 \| 0.1500 \| 24.5024 \| 21.4979 \| 24.0227 \| 24.0303 \| 19.0 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1,862	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-bbc-headline results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-bbc-headline This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 167 \| 3.6454 \| 22.4311 \| 5.9878 \| 20.118 \| 20.482 \| 18.9009 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	855	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-bbc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-bbc This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3238 - Rouge1: 21.2266 - Rouge2: 16.0927 - Rougel: 19.6785 - Rougelsum: 19.8849 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.4882 \| 1.0 \| 1001 \| 0.3238 \| 21.2266 \| 16.0927 \| 19.6785 \| 19.8849 \| 19.0 \| ### Framework versions - Transformers 4.12.0 - Pytorch 1.10.0 - Datasets 1.14.0 - Tokenizers 0.10.3
CAMeL-Lab/bert-base-arabic-camelbert-mix	[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "Arabic", "Dialect", "Egyptian", "Gulf", "Levantine", "Classical Arabic", "MSA", "Modern Standard Arabic", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	20,880	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum model-index: - name: t5-small-finetuned-xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 128 \| 2.9003 \| 19.4784 \| 2.8529 \| 14.7786 \| 15.0614 \| 18.9825 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	52	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8575 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 7.0964 \| 1.0 \| 2346 \| 7.0532 \| \| 6.9055 \| 2.0 \| 4692 \| 6.8710 \| \| 6.8574 \| 3.0 \| 7038 \| 6.8917 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	21	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5543972545286807 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8273 - Matthews Correlation: 0.5544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5256 \| 1.0 \| 535 \| 0.5419 \| 0.4248 \| \| 0.3486 \| 2.0 \| 1070 \| 0.5187 \| 0.4999 \| \| 0.2406 \| 3.0 \| 1605 \| 0.6580 \| 0.5054 \| \| 0.1692 \| 4.0 \| 2140 \| 0.7455 \| 0.5403 \| \| 0.1343 \| 5.0 \| 2675 \| 0.8273 \| 0.5544 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	133	null	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1112 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.5571 \| 1.0 \| 2249 \| 6.4684 \| \| 6.1921 \| 2.0 \| 4498 \| 6.1984 \| \| 6.0016 \| 3.0 \| 6747 \| 6.1112 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
CBreit00/DialoGPT_small_Rick	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-common_voice-es-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-common_voice-es-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - ES dataset. It achieves the following results on the evaluation set: - Loss: 0.1788 - Wer: 1.0239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| No log \| 0.02 \| 100 \| 6.6465 \| 1.0 \| \| No log \| 0.04 \| 200 \| 3.0150 \| 1.0 \| \| No log \| 0.05 \| 300 \| 2.8622 \| 1.0003 \| \| No log \| 0.07 \| 400 \| 0.9506 \| 0.9771 \| \| 5.1598 \| 0.09 \| 500 \| 0.4883 \| 1.0009 \| \| 5.1598 \| 0.11 \| 600 \| 0.3893 \| 1.0203 \| \| 5.1598 \| 0.13 \| 700 \| 0.3417 \| 1.0283 \| \| 5.1598 \| 0.14 \| 800 \| 0.3352 \| 1.0335 \| \| 5.1598 \| 0.16 \| 900 \| 0.2987 \| 1.0168 \| \| 0.3671 \| 0.18 \| 1000 \| 0.2921 \| 1.0159 \| \| 0.3671 \| 0.2 \| 1100 \| 0.2770 \| 1.0096 \| \| 0.3671 \| 0.22 \| 1200 \| 0.2790 \| 1.0398 \| \| 0.3671 \| 0.24 \| 1300 \| 0.2659 \| 1.0190 \| \| 0.3671 \| 0.25 \| 1400 \| 0.2657 \| 1.0528 \| \| 0.289 \| 0.27 \| 1500 \| 0.2556 \| 1.0301 \| \| 0.289 \| 0.29 \| 1600 \| 0.2514 \| 1.0193 \| \| 0.289 \| 0.31 \| 1700 \| 0.2708 \| 1.0699 \| \| 0.289 \| 0.33 \| 1800 \| 0.2455 \| 1.0723 \| \| 0.289 \| 0.34 \| 1900 \| 0.2456 \| 1.0100 \| \| 0.271 \| 0.36 \| 2000 \| 0.2338 \| 1.0533 \| \| 0.271 \| 0.38 \| 2100 \| 0.2479 \| 1.0128 \| \| 0.271 \| 0.4 \| 2200 \| 0.2483 \| 1.0386 \| \| 0.271 \| 0.42 \| 2300 \| 0.2436 \| 1.0528 \| \| 0.271 \| 0.43 \| 2400 \| 0.2382 \| 1.0476 \| \| 0.2634 \| 0.45 \| 2500 \| 0.2329 \| 1.0680 \| \| 0.2634 \| 0.47 \| 2600 \| 0.2433 \| 1.0581 \| \| 0.2634 \| 0.49 \| 2700 \| 0.2354 \| 1.0641 \| \| 0.2634 \| 0.51 \| 2800 \| 0.2318 \| 1.0504 \| \| 0.2634 \| 0.52 \| 2900 \| 0.2325 \| 1.0500 \| \| 0.2522 \| 0.54 \| 3000 \| 0.2344 \| 1.0380 \| \| 0.2522 \| 0.56 \| 3100 \| 0.2244 \| 1.0663 \| \| 0.2522 \| 0.58 \| 3200 \| 0.2340 \| 1.0647 \| \| 0.2522 \| 0.6 \| 3300 \| 0.2288 \| 1.0538 \| \| 0.2522 \| 0.61 \| 3400 \| 0.2212 \| 1.0614 \| \| 0.2468 \| 0.63 \| 3500 \| 0.2487 \| 1.0557 \| \| 0.2468 \| 0.65 \| 3600 \| 0.2330 \| 1.0510 \| \| 0.2468 \| 0.67 \| 3700 \| 0.2308 \| 1.0506 \| \| 0.2468 \| 0.69 \| 3800 \| 0.2320 \| 1.0451 \| \| 0.2468 \| 0.71 \| 3900 \| 0.2261 \| 1.0701 \| \| 0.2505 \| 0.72 \| 4000 \| 0.2281 \| 1.0713 \| \| 0.2505 \| 0.74 \| 4100 \| 0.2277 \| 1.0741 \| \| 0.2505 \| 0.76 \| 4200 \| 0.2253 \| 1.0814 \| \| 0.2505 \| 0.78 \| 4300 \| 0.2215 \| 1.0437 \| \| 0.2505 \| 0.8 \| 4400 \| 0.2220 \| 1.0557 \| \| 0.2434 \| 0.81 \| 4500 \| 0.2184 \| 1.0533 \| \| 0.2434 \| 0.83 \| 4600 \| 0.2222 \| 1.0819 \| \| 0.2434 \| 0.85 \| 4700 \| 0.2162 \| 1.0238 \| \| 0.2434 \| 0.87 \| 4800 \| 0.2132 \| 1.0457 \| \| 0.2434 \| 0.89 \| 4900 \| 0.2068 \| 1.0611 \| \| 0.2347 \| 0.9 \| 5000 \| 0.2166 \| 1.0332 \| \| 0.2347 \| 0.92 \| 5100 \| 0.2087 \| 1.0433 \| \| 0.2347 \| 0.94 \| 5200 \| 0.2100 \| 1.0292 \| \| 0.2347 \| 0.96 \| 5300 \| 0.2067 \| 1.0734 \| \| 0.2347 \| 0.98 \| 5400 \| 0.2148 \| 1.0279 \| \| 0.2333 \| 0.99 \| 5500 \| 0.2125 \| 1.0277 \| \| 0.2333 \| 1.01 \| 5600 \| 0.2054 \| 1.0453 \| \| 0.2333 \| 1.03 \| 5700 \| 0.2091 \| 1.0557 \| \| 0.2333 \| 1.05 \| 5800 \| 0.2086 \| 1.0239 \| \| 0.2333 \| 1.07 \| 5900 \| 0.2051 \| 1.0645 \| \| 0.2087 \| 1.09 \| 6000 \| 0.2103 \| 1.0240 \| \| 0.2087 \| 1.1 \| 6100 \| 0.2145 \| 1.0197 \| \| 0.2087 \| 1.12 \| 6200 \| 0.2136 \| 1.0248 \| \| 0.2087 \| 1.14 \| 6300 \| 0.2045 \| 1.0443 \| \| 0.2087 \| 1.16 \| 6400 \| 0.2089 \| 1.0397 \| \| 0.2013 \| 1.18 \| 6500 \| 0.2012 \| 1.0654 \| \| 0.2013 \| 1.19 \| 6600 \| 0.2054 \| 1.0414 \| \| 0.2013 \| 1.21 \| 6700 \| 0.2081 \| 1.0632 \| \| 0.2013 \| 1.23 \| 6800 \| 0.2104 \| 1.0190 \| \| 0.2013 \| 1.25 \| 6900 \| 0.2045 \| 1.0813 \| \| 0.2092 \| 1.27 \| 7000 \| 0.2096 \| 1.0751 \| \| 0.2092 \| 1.28 \| 7100 \| 0.2103 \| 1.0328 \| \| 0.2092 \| 1.3 \| 7200 \| 0.2044 \| 1.0011 \| \| 0.2092 \| 1.32 \| 7300 \| 0.2089 \| 1.0260 \| \| 0.2092 \| 1.34 \| 7400 \| 0.2063 \| 1.0551 \| \| 0.2076 \| 1.36 \| 7500 \| 0.2029 \| 1.0075 \| \| 0.2076 \| 1.37 \| 7600 \| 0.2040 \| 1.0528 \| \| 0.2076 \| 1.39 \| 7700 \| 0.2075 \| 1.0398 \| \| 0.2076 \| 1.41 \| 7800 \| 0.2023 \| 1.0231 \| \| 0.2076 \| 1.43 \| 7900 \| 0.2049 \| 1.0318 \| \| 0.2028 \| 1.45 \| 8000 \| 0.2072 \| 1.0763 \| \| 0.2028 \| 1.47 \| 8100 \| 0.2075 \| 1.0762 \| \| 0.2028 \| 1.48 \| 8200 \| 0.2052 \| 1.0838 \| \| 0.2028 \| 1.5 \| 8300 \| 0.2053 \| 1.0407 \| \| 0.2028 \| 1.52 \| 8400 \| 0.2066 \| 1.0266 \| \| 0.2025 \| 1.54 \| 8500 \| 0.2037 \| 1.0628 \| \| 0.2025 \| 1.56 \| 8600 \| 0.2010 \| 1.0351 \| \| 0.2025 \| 1.57 \| 8700 \| 0.1961 \| 1.0812 \| \| 0.2025 \| 1.59 \| 8800 \| 0.1963 \| 1.0868 \| \| 0.2025 \| 1.61 \| 8900 \| 0.2022 \| 1.0710 \| \| 0.1997 \| 1.63 \| 9000 \| 0.2051 \| 1.0764 \| \| 0.1997 \| 1.65 \| 9100 \| 0.1987 \| 1.0581 \| \| 0.1997 \| 1.66 \| 9200 \| 0.2051 \| 1.0611 \| \| 0.1997 \| 1.68 \| 9300 \| 0.1999 \| 1.0808 \| \| 0.1997 \| 1.7 \| 9400 \| 0.1972 \| 1.0703 \| \| 0.1983 \| 1.72 \| 9500 \| 0.1961 \| 1.0584 \| \| 0.1983 \| 1.74 \| 9600 \| 0.2031 \| 1.0938 \| \| 0.1983 \| 1.75 \| 9700 \| 0.2019 \| 1.0891 \| \| 0.1983 \| 1.77 \| 9800 \| 0.2006 \| 1.0542 \| \| 0.1983 \| 1.79 \| 9900 \| 0.1925 \| 1.0627 \| \| 0.1961 \| 1.81 \| 10000 \| 0.1976 \| 1.0751 \| \| 0.1961 \| 1.83 \| 10100 \| 0.2051 \| 1.0611 \| \| 0.1961 \| 1.85 \| 10200 \| 0.2037 \| 1.0656 \| \| 0.1961 \| 1.86 \| 10300 \| 0.2025 \| 1.0291 \| \| 0.1961 \| 1.88 \| 10400 \| 0.1977 \| 1.0525 \| \| 0.2025 \| 1.9 \| 10500 \| 0.2030 \| 1.0670 \| \| 0.2025 \| 1.92 \| 10600 \| 0.1980 \| 1.0765 \| \| 0.2025 \| 1.94 \| 10700 \| 0.1975 \| 1.0254 \| \| 0.2025 \| 1.95 \| 10800 \| 0.1986 \| 1.0636 \| \| 0.2025 \| 1.97 \| 10900 \| 0.1956 \| 1.0352 \| \| 0.2025 \| 1.99 \| 11000 \| 0.1954 \| 1.0265 \| \| 0.2025 \| 2.01 \| 11100 \| 0.1957 \| 1.0752 \| \| 0.2025 \| 2.03 \| 11200 \| 0.1943 \| 1.0784 \| \| 0.2025 \| 2.04 \| 11300 \| 0.1898 \| 1.0341 \| \| 0.2025 \| 2.06 \| 11400 \| 0.1921 \| 1.0301 \| \| 0.1805 \| 2.08 \| 11500 \| 0.1910 \| 1.0230 \| \| 0.1805 \| 2.1 \| 11600 \| 0.1961 \| 1.0203 \| \| 0.1805 \| 2.12 \| 11700 \| 0.1973 \| 1.0776 \| \| 0.1805 \| 2.13 \| 11800 \| 0.1876 \| 1.0788 \| \| 0.1805 \| 2.15 \| 11900 \| 0.1934 \| 1.0251 \| \| 0.177 \| 2.17 \| 12000 \| 0.1967 \| 1.0340 \| \| 0.177 \| 2.19 \| 12100 \| 0.1932 \| 1.0131 \| \| 0.177 \| 2.21 \| 12200 \| 0.1926 \| 1.0078 \| \| 0.177 \| 2.23 \| 12300 \| 0.1947 \| 0.9991 \| \| 0.177 \| 2.24 \| 12400 \| 0.1914 \| 1.0213 \| \| 0.1782 \| 2.26 \| 12500 \| 0.1962 \| 0.9882 \| \| 0.1782 \| 2.28 \| 12600 \| 0.1960 \| 1.0562 \| \| 0.1782 \| 2.3 \| 12700 \| 0.2006 \| 1.0401 \| \| 0.1782 \| 2.32 \| 12800 \| 0.1950 \| 1.0688 \| \| 0.1782 \| 2.33 \| 12900 \| 0.1920 \| 1.0435 \| \| 0.1796 \| 2.35 \| 13000 \| 0.1926 \| 1.0667 \| \| 0.1796 \| 2.37 \| 13100 \| 0.1949 \| 1.0859 \| \| 0.1796 \| 2.39 \| 13200 \| 0.1932 \| 1.0670 \| \| 0.1796 \| 2.41 \| 13300 \| 0.1882 \| 1.0663 \| \| 0.1796 \| 2.42 \| 13400 \| 0.1877 \| 1.0760 \| \| 0.1775 \| 2.44 \| 13500 \| 0.1893 \| 1.0859 \| \| 0.1775 \| 2.46 \| 13600 \| 0.1936 \| 1.0702 \| \| 0.1775 \| 2.48 \| 13700 \| 0.1871 \| 1.0414 \| \| 0.1775 \| 2.5 \| 13800 \| 0.1917 \| 1.0430 \| \| 0.1775 \| 2.51 \| 13900 \| 0.1922 \| 1.0422 \| \| 0.1778 \| 2.53 \| 14000 \| 0.1875 \| 1.0585 \| \| 0.1778 \| 2.55 \| 14100 \| 0.1876 \| 1.0603 \| \| 0.1778 \| 2.57 \| 14200 \| 0.1888 \| 1.0628 \| \| 0.1778 \| 2.59 \| 14300 \| 0.1948 \| 1.0782 \| \| 0.1778 \| 2.6 \| 14400 \| 0.1942 \| 1.0695 \| \| 0.1784 \| 2.62 \| 14500 \| 0.1842 \| 1.0863 \| \| 0.1784 \| 2.64 \| 14600 \| 0.1850 \| 1.0543 \| \| 0.1784 \| 2.66 \| 14700 \| 0.1824 \| 1.0683 \| \| 0.1784 \| 2.68 \| 14800 \| 0.1888 \| 1.0693 \| \| 0.1784 \| 2.7 \| 14900 \| 0.1871 \| 1.0175 \| \| 0.1753 \| 2.71 \| 15000 \| 0.1889 \| 1.0549 \| \| 0.1753 \| 2.73 \| 15100 \| 0.1865 \| 1.0544 \| \| 0.1753 \| 2.75 \| 15200 \| 0.1918 \| 1.0726 \| \| 0.1753 \| 2.77 \| 15300 \| 0.1964 \| 1.0915 \| \| 0.1753 \| 2.79 \| 15400 \| 0.1900 \| 1.0610 \| \| 0.1768 \| 2.8 \| 15500 \| 0.1894 \| 1.0763 \| \| 0.1768 \| 2.82 \| 15600 \| 0.1882 \| 1.0548 \| \| 0.1768 \| 2.84 \| 15700 \| 0.1861 \| 1.0902 \| \| 0.1768 \| 2.86 \| 15800 \| 0.1860 \| 1.0551 \| \| 0.1768 \| 2.88 \| 15900 \| 0.1879 \| 1.0581 \| \| 0.1761 \| 2.89 \| 16000 \| 0.1899 \| 1.0544 \| \| 0.1761 \| 2.91 \| 16100 \| 0.1860 \| 1.0530 \| \| 0.1761 \| 2.93 \| 16200 \| 0.1894 \| 1.0596 \| \| 0.1761 \| 2.95 \| 16300 \| 0.1835 \| 1.0394 \| \| 0.1761 \| 2.97 \| 16400 \| 0.1852 \| 1.0445 \| \| 0.1754 \| 2.98 \| 16500 \| 0.1847 \| 1.0390 \| \| 0.1754 \| 3.0 \| 16600 \| 0.1828 \| 1.0440 \| \| 0.1754 \| 3.02 \| 16700 \| 0.1869 \| 1.0560 \| \| 0.1754 \| 3.04 \| 16800 \| 0.1882 \| 1.0573 \| \| 0.1754 \| 3.06 \| 16900 \| 0.1912 \| 1.0600 \| \| 0.1592 \| 3.08 \| 17000 \| 0.1921 \| 1.0529 \| \| 0.1592 \| 3.09 \| 17100 \| 0.1881 \| 1.0175 \| \| 0.1592 \| 3.11 \| 17200 \| 0.1891 \| 1.0654 \| \| 0.1592 \| 3.13 \| 17300 \| 0.1889 \| 1.0687 \| \| 0.1592 \| 3.15 \| 17400 \| 0.1916 \| 1.0642 \| \| 0.1556 \| 3.17 \| 17500 \| 0.1850 \| 1.0295 \| \| 0.1556 \| 3.18 \| 17600 \| 0.1875 \| 1.0273 \| \| 0.1556 \| 3.2 \| 17700 \| 0.1894 \| 1.0051 \| \| 0.1556 \| 3.22 \| 17800 \| 0.1870 \| 1.0462 \| \| 0.1556 \| 3.24 \| 17900 \| 0.1831 \| 1.0308 \| \| 0.1557 \| 3.26 \| 18000 \| 0.1878 \| 1.0603 \| \| 0.1557 \| 3.27 \| 18100 \| 0.1850 \| 1.0566 \| \| 0.1557 \| 3.29 \| 18200 \| 0.1843 \| 1.0629 \| \| 0.1557 \| 3.31 \| 18300 \| 0.1886 \| 1.0378 \| \| 0.1557 \| 3.33 \| 18400 \| 0.1892 \| 1.0381 \| \| 0.159 \| 3.35 \| 18500 \| 0.1942 \| 1.0519 \| \| 0.159 \| 3.36 \| 18600 \| 0.1829 \| 1.0622 \| \| 0.159 \| 3.38 \| 18700 \| 0.1894 \| 1.0557 \| \| 0.159 \| 3.4 \| 18800 \| 0.1895 \| 1.0627 \| \| 0.159 \| 3.42 \| 18900 \| 0.1863 \| 1.0362 \| \| 0.1582 \| 3.44 \| 19000 \| 0.1888 \| 1.0491 \| \| 0.1582 \| 3.46 \| 19100 \| 0.1854 \| 1.0483 \| \| 0.1582 \| 3.47 \| 19200 \| 0.1797 \| 0.9787 \| \| 0.1582 \| 3.49 \| 19300 \| 0.1785 \| 1.0086 \| \| 0.1582 \| 3.51 \| 19400 \| 0.1797 \| 0.9915 \| \| 0.1507 \| 3.53 \| 19500 \| 0.1873 \| 1.0266 \| \| 0.1507 \| 3.55 \| 19600 \| 0.1838 \| 1.0299 \| \| 0.1507 \| 3.56 \| 19700 \| 0.1817 \| 1.0355 \| \| 0.1507 \| 3.58 \| 19800 \| 0.1819 \| 1.0271 \| \| 0.1507 \| 3.6 \| 19900 \| 0.1883 \| 1.0248 \| \| 0.1601 \| 3.62 \| 20000 \| 0.1823 \| 1.0406 \| \| 0.1601 \| 3.64 \| 20100 \| 0.1801 \| 1.0261 \| \| 0.1601 \| 3.65 \| 20200 \| 0.1783 \| 1.0329 \| \| 0.1601 \| 3.67 \| 20300 \| 0.1857 \| 1.0162 \| \| 0.1601 \| 3.69 \| 20400 \| 0.1814 \| 1.0212 \| \| 0.1552 \| 3.71 \| 20500 \| 0.1837 \| 1.0232 \| \| 0.1552 \| 3.73 \| 20600 \| 0.1843 \| 1.0314 \| \| 0.1552 \| 3.74 \| 20700 \| 0.1842 \| 1.0258 \| \| 0.1552 \| 3.76 \| 20800 \| 0.1821 \| 1.0479 \| \| 0.1552 \| 3.78 \| 20900 \| 0.1864 \| 1.0459 \| \| 0.1576 \| 3.8 \| 21000 \| 0.1831 \| 1.0364 \| \| 0.1576 \| 3.82 \| 21100 \| 0.1852 \| 1.0271 \| \| 0.1576 \| 3.83 \| 21200 \| 0.1865 \| 1.0204 \| \| 0.1576 \| 3.85 \| 21300 \| 0.1794 \| 1.0324 \| \| 0.1576 \| 3.87 \| 21400 \| 0.1826 \| 1.0315 \| \| 0.1585 \| 3.89 \| 21500 \| 0.1824 \| 1.0327 \| \| 0.1585 \| 3.91 \| 21600 \| 0.1838 \| 1.0208 \| \| 0.1585 \| 3.93 \| 21700 \| 0.1850 \| 1.0199 \| \| 0.1585 \| 3.94 \| 21800 \| 0.1841 \| 1.0050 \| \| 0.1585 \| 3.96 \| 21900 \| 0.1783 \| 1.0003 \| \| 0.1572 \| 3.98 \| 22000 \| 0.1787 \| 1.0115 \| \| 0.1572 \| 4.0 \| 22100 \| 0.1810 \| 1.0235 \| \| 0.1572 \| 4.02 \| 22200 \| 0.1763 \| 1.0191 \| \| 0.1572 \| 4.03 \| 22300 \| 0.1764 \| 1.0332 \| \| 0.1572 \| 4.05 \| 22400 \| 0.1794 \| 1.0429 \| \| 0.1406 \| 4.07 \| 22500 \| 0.1905 \| 1.0288 \| \| 0.1406 \| 4.09 \| 22600 \| 0.1776 \| 1.0244 \| \| 0.1406 \| 4.11 \| 22700 \| 0.1782 \| 1.0451 \| \| 0.1406 \| 4.12 \| 22800 \| 0.1771 \| 1.0387 \| \| 0.1406 \| 4.14 \| 22900 \| 0.1788 \| 1.0435 \| \| 0.14 \| 4.16 \| 23000 \| 0.1792 \| 1.0421 \| \| 0.14 \| 4.18 \| 23100 \| 0.1841 \| 1.0241 \| \| 0.14 \| 4.2 \| 23200 \| 0.1769 \| 1.0546 \| \| 0.14 \| 4.21 \| 23300 \| 0.1815 \| 1.0602 \| \| 0.14 \| 4.23 \| 23400 \| 0.1784 \| 1.0369 \| \| 0.1394 \| 4.25 \| 23500 \| 0.1809 \| 1.0406 \| \| 0.1394 \| 4.27 \| 23600 \| 0.1744 \| 1.0133 \| \| 0.1394 \| 4.29 \| 23700 \| 0.1771 \| 1.0214 \| \| 0.1394 \| 4.31 \| 23800 \| 0.1765 \| 1.0064 \| \| 0.1394 \| 4.32 \| 23900 \| 0.1793 \| 1.0200 \| \| 0.14 \| 4.34 \| 24000 \| 0.1776 \| 1.0352 \| \| 0.14 \| 4.36 \| 24100 \| 0.1775 \| 1.0294 \| \| 0.14 \| 4.38 \| 24200 \| 0.1763 \| 1.0213 \| \| 0.14 \| 4.4 \| 24300 \| 0.1697 \| 1.0302 \| \| 0.14 \| 4.41 \| 24400 \| 0.1771 \| 1.0259 \| \| 0.1408 \| 4.43 \| 24500 \| 0.1747 \| 1.0409 \| \| 0.1408 \| 4.45 \| 24600 \| 0.1769 \| 1.0278 \| \| 0.1408 \| 4.47 \| 24700 \| 0.1767 \| 1.0190 \| \| 0.1408 \| 4.49 \| 24800 \| 0.1745 \| 1.0281 \| \| 0.1408 \| 4.5 \| 24900 \| 0.1738 \| 1.0356 \| \| 0.1391 \| 4.52 \| 25000 \| 0.1781 \| 1.0429 \| \| 0.1391 \| 4.54 \| 25100 \| 0.1784 \| 1.0076 \| \| 0.1391 \| 4.56 \| 25200 \| 0.1771 \| 1.0157 \| \| 0.1391 \| 4.58 \| 25300 \| 0.1758 \| 1.0337 \| \| 0.1391 \| 4.59 \| 25400 \| 0.1758 \| 1.0466 \| \| 0.1398 \| 4.61 \| 25500 \| 0.1724 \| 1.0403 \| \| 0.1398 \| 4.63 \| 25600 \| 0.1765 \| 1.0481 \| \| 0.1398 \| 4.65 \| 25700 \| 0.1757 \| 1.0320 \| \| 0.1398 \| 4.67 \| 25800 \| 0.1814 \| 1.0479 \| \| 0.1398 \| 4.69 \| 25900 \| 0.1713 \| 1.0251 \| \| 0.1427 \| 4.7 \| 26000 \| 0.1735 \| 1.0340 \| \| 0.1427 \| 4.72 \| 26100 \| 0.1765 \| 1.0358 \| \| 0.1427 \| 4.74 \| 26200 \| 0.1731 \| 1.0220 \| \| 0.1427 \| 4.76 \| 26300 \| 0.1769 \| 1.0261 \| \| 0.1427 \| 4.78 \| 26400 \| 0.1747 \| 1.0139 \| \| 0.1424 \| 4.79 \| 26500 \| 0.1791 \| 1.0406 \| \| 0.1424 \| 4.81 \| 26600 \| 0.1735 \| 1.0497 \| \| 0.1424 \| 4.83 \| 26700 \| 0.1710 \| 1.0433 \| \| 0.1424 \| 4.85 \| 26800 \| 0.1771 \| 1.0002 \| \| 0.1424 \| 4.87 \| 26900 \| 0.1748 \| 1.0046 \| \| 0.1419 \| 4.88 \| 27000 \| 0.1794 \| 1.0332 \| \| 0.1419 \| 4.9 \| 27100 \| 0.1772 \| 1.0558 \| \| 0.1419 \| 4.92 \| 27200 \| 0.1757 \| 1.0477 \| \| 0.1419 \| 4.94 \| 27300 \| 0.1735 \| 1.0324 \| \| 0.1419 \| 4.96 \| 27400 \| 0.1758 \| 1.0260 \| \| 0.1433 \| 4.97 \| 27500 \| 0.1767 \| 1.0422 \| \| 0.1433 \| 4.99 \| 27600 \| 0.1695 \| 1.0386 \| \| 0.1433 \| 5.01 \| 27700 \| 0.1763 \| 1.0571 \| \| 0.1433 \| 5.03 \| 27800 \| 0.1743 \| 1.0367 \| \| 0.1433 \| 5.05 \| 27900 \| 0.1804 \| 1.0255 \| \| 0.1306 \| 5.07 \| 28000 \| 0.1803 \| 1.0377 \| \| 0.1306 \| 5.08 \| 28100 \| 0.1750 \| 1.0552 \| \| 0.1306 \| 5.1 \| 28200 \| 0.1743 \| 1.0512 \| \| 0.1306 \| 5.12 \| 28300 \| 0.1777 \| 1.0584 \| \| 0.1306 \| 5.14 \| 28400 \| 0.1726 \| 1.0374 \| \| 0.123 \| 5.16 \| 28500 \| 0.1776 \| 1.0439 \| \| 0.123 \| 5.17 \| 28600 \| 0.1759 \| 1.0682 \| \| 0.123 \| 5.19 \| 28700 \| 0.1724 \| 1.0511 \| \| 0.123 \| 5.21 \| 28800 \| 0.1677 \| 1.0560 \| \| 0.123 \| 5.23 \| 28900 \| 0.1699 \| 1.0421 \| \| 0.1217 \| 5.25 \| 29000 \| 0.1803 \| 1.0370 \| \| 0.1217 \| 5.26 \| 29100 \| 0.1770 \| 1.0474 \| \| 0.1217 \| 5.28 \| 29200 \| 0.1733 \| 1.0332 \| \| 0.1217 \| 5.3 \| 29300 \| 0.1746 \| 1.0158 \| \| 0.1217 \| 5.32 \| 29400 \| 0.1763 \| 1.0341 \| \| 0.1246 \| 5.34 \| 29500 \| 0.1775 \| 1.0348 \| \| 0.1246 \| 5.35 \| 29600 \| 0.1730 \| 1.0492 \| \| 0.1246 \| 5.37 \| 29700 \| 0.1730 \| 1.0503 \| \| 0.1246 \| 5.39 \| 29800 \| 0.1727 \| 1.0437 \| \| 0.1246 \| 5.41 \| 29900 \| 0.1744 \| 1.0539 \| \| 0.127 \| 5.43 \| 30000 \| 0.1748 \| 1.0463 \| \| 0.127 \| 5.44 \| 30100 \| 0.1746 \| 1.0555 \| \| 0.127 \| 5.46 \| 30200 \| 0.1810 \| 1.0558 \| \| 0.127 \| 5.48 \| 30300 \| 0.1773 \| 1.0407 \| \| 0.127 \| 5.5 \| 30400 \| 0.1722 \| 1.0489 \| \| 0.1276 \| 5.52 \| 30500 \| 0.1720 \| 1.0520 \| \| 0.1276 \| 5.54 \| 30600 \| 0.1777 \| 1.0347 \| \| 0.1276 \| 5.55 \| 30700 \| 0.1685 \| 1.0347 \| \| 0.1276 \| 5.57 \| 30800 \| 0.1659 \| 1.0338 \| \| 0.1276 \| 5.59 \| 30900 \| 0.1756 \| 1.0228 \| \| 0.1246 \| 5.61 \| 31000 \| 0.1717 \| 1.0409 \| \| 0.1246 \| 5.63 \| 31100 \| 0.1764 \| 1.0202 \| \| 0.1246 \| 5.64 \| 31200 \| 0.1693 \| 1.0314 \| \| 0.1246 \| 5.66 \| 31300 \| 0.1731 \| 1.0319 \| \| 0.1246 \| 5.68 \| 31400 \| 0.1688 \| 1.0380 \| \| 0.1271 \| 5.7 \| 31500 \| 0.1671 \| 1.0350 \| \| 0.1271 \| 5.72 \| 31600 \| 0.1676 \| 1.0430 \| \| 0.1271 \| 5.73 \| 31700 \| 0.1656 \| 1.0441 \| \| 0.1271 \| 5.75 \| 31800 \| 0.1664 \| 1.0403 \| \| 0.1271 \| 5.77 \| 31900 \| 0.1691 \| 1.0152 \| \| 0.1259 \| 5.79 \| 32000 \| 0.1702 \| 1.0018 \| \| 0.1259 \| 5.81 \| 32100 \| 0.1664 \| 1.0246 \| \| 0.1259 \| 5.82 \| 32200 \| 0.1737 \| 1.0340 \| \| 0.1259 \| 5.84 \| 32300 \| 0.1742 \| 1.0449 \| \| 0.1259 \| 5.86 \| 32400 \| 0.1707 \| 1.0279 \| \| 0.1273 \| 5.88 \| 32500 \| 0.1697 \| 1.0471 \| \| 0.1273 \| 5.9 \| 32600 \| 0.1668 \| 1.0322 \| \| 0.1273 \| 5.92 \| 32700 \| 0.1706 \| 1.0378 \| \| 0.1273 \| 5.93 \| 32800 \| 0.1704 \| 1.0350 \| \| 0.1273 \| 5.95 \| 32900 \| 0.1725 \| 1.0244 \| \| 0.123 \| 5.97 \| 33000 \| 0.1678 \| 1.0447 \| \| 0.123 \| 5.99 \| 33100 \| 0.1681 \| 1.0438 \| \| 0.123 \| 6.01 \| 33200 \| 0.1689 \| 1.0297 \| \| 0.123 \| 6.02 \| 33300 \| 0.1690 \| 1.0333 \| \| 0.123 \| 6.04 \| 33400 \| 0.1734 \| 1.0296 \| \| 0.1163 \| 6.06 \| 33500 \| 0.1748 \| 1.0307 \| \| 0.1163 \| 6.08 \| 33600 \| 0.1715 \| 1.0123 \| \| 0.1163 \| 6.1 \| 33700 \| 0.1668 \| 1.0117 \| \| 0.1163 \| 6.11 \| 33800 \| 0.1690 \| 1.0230 \| \| 0.1163 \| 6.13 \| 33900 \| 0.1693 \| 1.0166 \| \| 0.1101 \| 6.15 \| 34000 \| 0.1728 \| 1.0162 \| \| 0.1101 \| 6.17 \| 34100 \| 0.1683 \| 1.0107 \| \| 0.1101 \| 6.19 \| 34200 \| 0.1703 \| 0.9814 \| \| 0.1101 \| 6.2 \| 34300 \| 0.1692 \| 1.0007 \| \| 0.1101 \| 6.22 \| 34400 \| 0.1690 \| 1.0000 \| \| 0.1118 \| 6.24 \| 34500 \| 0.1734 \| 0.9972 \| \| 0.1118 \| 6.26 \| 34600 \| 0.1739 \| 1.0096 \| \| 0.1118 \| 6.28 \| 34700 \| 0.1749 \| 1.0047 \| \| 0.1118 \| 6.3 \| 34800 \| 0.1709 \| 1.0111 \| \| 0.1118 \| 6.31 \| 34900 \| 0.1717 \| 1.0179 \| \| 0.1153 \| 6.33 \| 35000 \| 0.1690 \| 1.0155 \| \| 0.1153 \| 6.35 \| 35100 \| 0.1710 \| 1.0144 \| \| 0.1153 \| 6.37 \| 35200 \| 0.1719 \| 1.0030 \| \| 0.1153 \| 6.39 \| 35300 \| 0.1690 \| 1.0272 \| \| 0.1153 \| 6.4 \| 35400 \| 0.1673 \| 1.0103 \| \| 0.1106 \| 6.42 \| 35500 \| 0.1710 \| 1.0222 \| \| 0.1106 \| 6.44 \| 35600 \| 0.1747 \| 1.0173 \| \| 0.1106 \| 6.46 \| 35700 \| 0.1721 \| 0.9933 \| \| 0.1106 \| 6.48 \| 35800 \| 0.1670 \| 1.0184 \| \| 0.1106 \| 6.49 \| 35900 \| 0.1714 \| 1.0122 \| \| 0.1116 \| 6.51 \| 36000 \| 0.1717 \| 1.0035 \| \| 0.1116 \| 6.53 \| 36100 \| 0.1685 \| 1.0099 \| \| 0.1116 \| 6.55 \| 36200 \| 0.1687 \| 1.0288 \| \| 0.1116 \| 6.57 \| 36300 \| 0.1664 \| 1.0314 \| \| 0.1116 \| 6.58 \| 36400 \| 0.1665 \| 1.0264 \| \| 0.1128 \| 6.6 \| 36500 \| 0.1681 \| 1.0420 \| \| 0.1128 \| 6.62 \| 36600 \| 0.1682 \| 1.0409 \| \| 0.1128 \| 6.64 \| 36700 \| 0.1717 \| 1.0271 \| \| 0.1128 \| 6.66 \| 36800 \| 0.1717 \| 1.0166 \| \| 0.1128 \| 6.68 \| 36900 \| 0.1755 \| 1.0175 \| \| 0.1134 \| 6.69 \| 37000 \| 0.1623 \| 1.0185 \| \| 0.1134 \| 6.71 \| 37100 \| 0.1674 \| 1.0302 \| \| 0.1134 \| 6.73 \| 37200 \| 0.1633 \| 1.0325 \| \| 0.1134 \| 6.75 \| 37300 \| 0.1628 \| 1.0228 \| \| 0.1134 \| 6.77 \| 37400 \| 0.1636 \| 1.0243 \| \| 0.1102 \| 6.78 \| 37500 \| 0.1667 \| 1.0282 \| \| 0.1102 \| 6.8 \| 37600 \| 0.1623 \| 1.0212 \| \| 0.1102 \| 6.82 \| 37700 \| 0.1639 \| 1.0140 \| \| 0.1102 \| 6.84 \| 37800 \| 0.1587 \| 1.0258 \| \| 0.1102 \| 6.86 \| 37900 \| 0.1610 \| 1.0087 \| \| 0.1113 \| 6.87 \| 38000 \| 0.1647 \| 1.0199 \| \| 0.1113 \| 6.89 \| 38100 \| 0.1609 \| 1.0054 \| \| 0.1113 \| 6.91 \| 38200 \| 0.1602 \| 1.0145 \| \| 0.1113 \| 6.93 \| 38300 \| 0.1602 \| 1.0144 \| \| 0.1113 \| 6.95 \| 38400 \| 0.1602 \| 1.0375 \| \| 0.1071 \| 6.96 \| 38500 \| 0.1592 \| 1.0259 \| \| 0.1071 \| 6.98 \| 38600 \| 0.1612 \| 1.0236 \| \| 0.1071 \| 7.0 \| 38700 \| 0.1621 \| 1.0277 \| \| 0.1071 \| 7.02 \| 38800 \| 0.1669 \| 1.0367 \| \| 0.1071 \| 7.04 \| 38900 \| 0.1742 \| 1.0484 \| \| 0.1062 \| 7.05 \| 39000 \| 0.1752 \| 1.0302 \| \| 0.1062 \| 7.07 \| 39100 \| 0.1676 \| 1.0244 \| \| 0.1062 \| 7.09 \| 39200 \| 0.1723 \| 1.0300 \| \| 0.1062 \| 7.11 \| 39300 \| 0.1727 \| 1.0294 \| \| 0.1062 \| 7.13 \| 39400 \| 0.1711 \| 1.0255 \| \| 0.1021 \| 7.15 \| 39500 \| 0.1699 \| 1.0471 \| \| 0.1021 \| 7.16 \| 39600 \| 0.1682 \| 1.0426 \| \| 0.1021 \| 7.18 \| 39700 \| 0.1713 \| 1.0233 \| \| 0.1021 \| 7.2 \| 39800 \| 0.1682 \| 1.0259 \| \| 0.1021 \| 7.22 \| 39900 \| 0.1710 \| 1.0162 \| \| 0.103 \| 7.24 \| 40000 \| 0.1725 \| 1.0283 \| \| 0.103 \| 7.25 \| 40100 \| 0.1729 \| 1.0264 \| \| 0.103 \| 7.27 \| 40200 \| 0.1665 \| 1.0451 \| \| 0.103 \| 7.29 \| 40300 \| 0.1671 \| 1.0386 \| \| 0.103 \| 7.31 \| 40400 \| 0.1671 \| 1.0316 \| \| 0.0981 \| 7.33 \| 40500 \| 0.1708 \| 1.0257 \| \| 0.0981 \| 7.34 \| 40600 \| 0.1642 \| 1.0152 \| \| 0.0981 \| 7.36 \| 40700 \| 0.1707 \| 1.0110 \| \| 0.0981 \| 7.38 \| 40800 \| 0.1675 \| 1.0186 \| \| 0.0981 \| 7.4 \| 40900 \| 0.1702 \| 1.0123 \| \| 0.1005 \| 7.42 \| 41000 \| 0.1699 \| 1.0159 \| \| 0.1005 \| 7.43 \| 41100 \| 0.1703 \| 1.0219 \| \| 0.1005 \| 7.45 \| 41200 \| 0.1707 \| 1.0194 \| \| 0.1005 \| 7.47 \| 41300 \| 0.1644 \| 1.0016 \| \| 0.1005 \| 7.49 \| 41400 \| 0.1716 \| 0.9941 \| \| 0.1021 \| 7.51 \| 41500 \| 0.1670 \| 1.0159 \| \| 0.1021 \| 7.53 \| 41600 \| 0.1667 \| 1.0033 \| \| 0.1021 \| 7.54 \| 41700 \| 0.1667 \| 1.0176 \| \| 0.1021 \| 7.56 \| 41800 \| 0.1679 \| 1.0194 \| \| 0.1021 \| 7.58 \| 41900 \| 0.1632 \| 1.0418 \| \| 0.0963 \| 7.6 \| 42000 \| 0.1712 \| 1.0152 \| \| 0.0963 \| 7.62 \| 42100 \| 0.1632 \| 1.0364 \| \| 0.0963 \| 7.63 \| 42200 \| 0.1702 \| 1.0229 \| \| 0.0963 \| 7.65 \| 42300 \| 0.1655 \| 1.0179 \| \| 0.0963 \| 7.67 \| 42400 \| 0.1698 \| 1.0329 \| \| 0.1014 \| 7.69 \| 42500 \| 0.1691 \| 1.0398 \| \| 0.1014 \| 7.71 \| 42600 \| 0.1638 \| 1.0487 \| \| 0.1014 \| 7.72 \| 42700 \| 0.1617 \| 1.0210 \| \| 0.1014 \| 7.74 \| 42800 \| 0.1648 \| 1.0124 \| \| 0.1014 \| 7.76 \| 42900 \| 0.1608 \| 1.0202 \| \| 0.1008 \| 7.78 \| 43000 \| 0.1611 \| 1.0353 \| \| 0.1008 \| 7.8 \| 43100 \| 0.1633 \| 1.0319 \| \| 0.1008 \| 7.81 \| 43200 \| 0.1640 \| 1.0032 \| \| 0.1008 \| 7.83 \| 43300 \| 0.1589 \| 0.9985 \| \| 0.1008 \| 7.85 \| 43400 \| 0.1630 \| 0.9975 \| \| 0.0988 \| 7.87 \| 43500 \| 0.1604 \| 1.0053 \| \| 0.0988 \| 7.89 \| 43600 \| 0.1687 \| 1.0063 \| \| 0.0988 \| 7.91 \| 43700 \| 0.1619 \| 1.0096 \| \| 0.0988 \| 7.92 \| 43800 \| 0.1565 \| 0.9901 \| \| 0.0988 \| 7.94 \| 43900 \| 0.1619 \| 0.9742 \| \| 0.102 \| 7.96 \| 44000 \| 0.1598 \| 0.9593 \| \| 0.102 \| 7.98 \| 44100 \| 0.1635 \| 0.9718 \| \| 0.102 \| 8.0 \| 44200 \| 0.1624 \| 0.9903 \| \| 0.102 \| 8.01 \| 44300 \| 0.1605 \| 0.9882 \| \| 0.102 \| 8.03 \| 44400 \| 0.1657 \| 1.0128 \| \| 0.0961 \| 8.05 \| 44500 \| 0.1651 \| 1.0155 \| \| 0.0961 \| 8.07 \| 44600 \| 0.1680 \| 1.0194 \| \| 0.0961 \| 8.09 \| 44700 \| 0.1694 \| 1.0112 \| \| 0.0961 \| 8.1 \| 44800 \| 0.1665 \| 1.0073 \| \| 0.0961 \| 8.12 \| 44900 \| 0.1612 \| 1.0200 \| \| 0.0894 \| 8.14 \| 45000 \| 0.1652 \| 1.0337 \| \| 0.0894 \| 8.16 \| 45100 \| 0.1626 \| 1.0086 \| \| 0.0894 \| 8.18 \| 45200 \| 0.1639 \| 1.0083 \| \| 0.0894 \| 8.19 \| 45300 \| 0.1634 \| 1.0223 \| \| 0.0894 \| 8.21 \| 45400 \| 0.1631 \| 1.0339 \| \| 0.0887 \| 8.23 \| 45500 \| 0.1640 \| 1.0311 \| \| 0.0887 \| 8.25 \| 45600 \| 0.1661 \| 1.0264 \| \| 0.0887 \| 8.27 \| 45700 \| 0.1650 \| 1.0315 \| \| 0.0887 \| 8.29 \| 45800 \| 0.1624 \| 1.0390 \| \| 0.0887 \| 8.3 \| 45900 \| 0.1624 \| 1.0350 \| \| 0.0884 \| 8.32 \| 46000 \| 0.1615 \| 1.0318 \| \| 0.0884 \| 8.34 \| 46100 \| 0.1628 \| 1.0410 \| \| 0.0884 \| 8.36 \| 46200 \| 0.1627 \| 1.0429 \| \| 0.0884 \| 8.38 \| 46300 \| 0.1644 \| 1.0320 \| \| 0.0884 \| 8.39 \| 46400 \| 0.1633 \| 1.0177 \| \| 0.0893 \| 8.41 \| 46500 \| 0.1654 \| 1.0189 \| \| 0.0893 \| 8.43 \| 46600 \| 0.1598 \| 1.0154 \| \| 0.0893 \| 8.45 \| 46700 \| 0.1618 \| 1.0250 \| \| 0.0893 \| 8.47 \| 46800 \| 0.1639 \| 1.0402 \| \| 0.0893 \| 8.48 \| 46900 \| 0.1616 \| 1.0336 \| \| 0.0869 \| 8.5 \| 47000 \| 0.1613 \| 1.0296 \| \| 0.0869 \| 8.52 \| 47100 \| 0.1648 \| 1.0568 \| \| 0.0869 \| 8.54 \| 47200 \| 0.1625 \| 1.0256 \| \| 0.0869 \| 8.56 \| 47300 \| 0.1609 \| 1.0390 \| \| 0.0869 \| 8.57 \| 47400 \| 0.1606 \| 1.0450 \| \| 0.0894 \| 8.59 \| 47500 \| 0.1605 \| 1.0445 \| \| 0.0894 \| 8.61 \| 47600 \| 0.1660 \| 1.0402 \| \| 0.0894 \| 8.63 \| 47700 \| 0.1618 \| 1.0444 \| \| 0.0894 \| 8.65 \| 47800 \| 0.1669 \| 1.0333 \| \| 0.0894 \| 8.66 \| 47900 \| 0.1627 \| 1.0364 \| \| 0.0885 \| 8.68 \| 48000 \| 0.1616 \| 1.0334 \| \| 0.0885 \| 8.7 \| 48100 \| 0.1626 \| 1.0564 \| \| 0.0885 \| 8.72 \| 48200 \| 0.1624 \| 1.0396 \| \| 0.0885 \| 8.74 \| 48300 \| 0.1623 \| 1.0396 \| \| 0.0885 \| 8.76 \| 48400 \| 0.1612 \| 1.0112 \| \| 0.0888 \| 8.77 \| 48500 \| 0.1638 \| 1.0292 \| \| 0.0888 \| 8.79 \| 48600 \| 0.1639 \| 0.9988 \| \| 0.0888 \| 8.81 \| 48700 \| 0.1618 \| 1.0127 \| \| 0.0888 \| 8.83 \| 48800 \| 0.1584 \| 1.0042 \| \| 0.0888 \| 8.85 \| 48900 \| 0.1615 \| 1.0041 \| \| 0.0887 \| 8.86 \| 49000 \| 0.1637 \| 1.0269 \| \| 0.0887 \| 8.88 \| 49100 \| 0.1627 \| 0.9989 \| \| 0.0887 \| 8.9 \| 49200 \| 0.1583 \| 1.0104 \| \| 0.0887 \| 8.92 \| 49300 \| 0.1600 \| 1.0214 \| \| 0.0887 \| 8.94 \| 49400 \| 0.1599 \| 1.0126 \| \| 0.0893 \| 8.95 \| 49500 \| 0.1595 \| 1.0516 \| \| 0.0893 \| 8.97 \| 49600 \| 0.1625 \| 1.0464 \| \| 0.0893 \| 8.99 \| 49700 \| 0.1595 \| 1.0361 \| \| 0.0893 \| 9.01 \| 49800 \| 0.1614 \| 1.0469 \| \| 0.0893 \| 9.03 \| 49900 \| 0.1612 \| 1.0304 \| \| 0.0834 \| 9.04 \| 50000 \| 0.1643 \| 1.0335 \| \| 0.0834 \| 9.06 \| 50100 \| 0.1640 \| 1.0175 \| \| 0.0834 \| 9.08 \| 50200 \| 0.1655 \| 1.0264 \| \| 0.0834 \| 9.1 \| 50300 \| 0.1678 \| 1.0243 \| \| 0.0834 \| 9.12 \| 50400 \| 0.1659 \| 1.0145 \| \| 0.079 \| 9.14 \| 50500 \| 0.1644 \| 1.0316 \| \| 0.079 \| 9.15 \| 50600 \| 0.1630 \| 1.0326 \| \| 0.079 \| 9.17 \| 50700 \| 0.1634 \| 1.0154 \| \| 0.079 \| 9.19 \| 50800 \| 0.1697 \| 1.0095 \| \| 0.079 \| 9.21 \| 50900 \| 0.1678 \| 1.0050 \| \| 0.078 \| 9.23 \| 51000 \| 0.1626 \| 1.0159 \| \| 0.078 \| 9.24 \| 51100 \| 0.1666 \| 1.0238 \| \| 0.078 \| 9.26 \| 51200 \| 0.1644 \| 1.0244 \| \| 0.078 \| 9.28 \| 51300 \| 0.1655 \| 1.0345 \| \| 0.078 \| 9.3 \| 51400 \| 0.1615 \| 1.0237 \| \| 0.0776 \| 9.32 \| 51500 \| 0.1664 \| 1.0180 \| \| 0.0776 \| 9.33 \| 51600 \| 0.1603 \| 1.0208 \| \| 0.0776 \| 9.35 \| 51700 \| 0.1594 \| 1.0230 \| \| 0.0776 \| 9.37 \| 51800 \| 0.1622 \| 1.0201 \| \| 0.0776 \| 9.39 \| 51900 \| 0.1596 \| 1.0039 \| \| 0.0782 \| 9.41 \| 52000 \| 0.1645 \| 1.0204 \| \| 0.0782 \| 9.42 \| 52100 \| 0.1640 \| 1.0318 \| \| 0.0782 \| 9.44 \| 52200 \| 0.1621 \| 1.0290 \| \| 0.0782 \| 9.46 \| 52300 \| 0.1638 \| 1.0318 \| \| 0.0782 \| 9.48 \| 52400 \| 0.1613 \| 1.0217 \| \| 0.0782 \| 9.5 \| 52500 \| 0.1609 \| 1.0261 \| \| 0.0782 \| 9.52 \| 52600 \| 0.1625 \| 1.0101 \| \| 0.0782 \| 9.53 \| 52700 \| 0.1613 \| 1.0058 \| \| 0.0782 \| 9.55 \| 52800 \| 0.1599 \| 1.0068 \| \| 0.0782 \| 9.57 \| 52900 \| 0.1600 \| 1.0110 \| \| 0.0797 \| 9.59 \| 53000 \| 0.1594 \| 1.0171 \| \| 0.0797 \| 9.61 \| 53100 \| 0.1583 \| 1.0124 \| \| 0.0797 \| 9.62 \| 53200 \| 0.1646 \| 1.0093 \| \| 0.0797 \| 9.64 \| 53300 \| 0.1580 \| 1.0201 \| \| 0.0797 \| 9.66 \| 53400 \| 0.1599 \| 1.0207 \| \| 0.0783 \| 9.68 \| 53500 \| 0.1577 \| 1.0226 \| \| 0.0783 \| 9.7 \| 53600 \| 0.1593 \| 1.0160 \| \| 0.0783 \| 9.71 \| 53700 \| 0.1570 \| 1.0173 \| \| 0.0783 \| 9.73 \| 53800 \| 0.1614 \| 1.0299 \| \| 0.0783 \| 9.75 \| 53900 \| 0.1610 \| 1.0184 \| \| 0.0779 \| 9.77 \| 54000 \| 0.1606 \| 1.0173 \| \| 0.0779 \| 9.79 \| 54100 \| 0.1577 \| 1.0032 \| \| 0.0779 \| 9.8 \| 54200 \| 0.1590 \| 1.0070 \| \| 0.0779 \| 9.82 \| 54300 \| 0.1580 \| 1.0257 \| \| 0.0779 \| 9.84 \| 54400 \| 0.1592 \| 1.0108 \| \| 0.0778 \| 9.86 \| 54500 \| 0.1617 \| 0.9907 \| \| 0.0778 \| 9.88 \| 54600 \| 0.1605 \| 1.0189 \| \| 0.0778 \| 9.89 \| 54700 \| 0.1605 \| 1.0177 \| \| 0.0778 \| 9.91 \| 54800 \| 0.1536 \| 1.0275 \| \| 0.0778 \| 9.93 \| 54900 \| 0.1658 \| 1.0282 \| \| 0.0777 \| 9.95 \| 55000 \| 0.1543 \| 1.0385 \| \| 0.0777 \| 9.97 \| 55100 \| 0.1559 \| 1.0375 \| \| 0.0777 \| 9.99 \| 55200 \| 0.1590 \| 1.0215 \| \| 0.0777 \| 10.0 \| 55300 \| 0.1624 \| 1.0242 \| \| 0.0777 \| 10.02 \| 55400 \| 0.1635 \| 1.0244 \| \| 0.0712 \| 10.04 \| 55500 \| 0.1629 \| 1.0298 \| \| 0.0712 \| 10.06 \| 55600 \| 0.1601 \| 1.0299 \| \| 0.0712 \| 10.08 \| 55700 \| 0.1625 \| 1.0117 \| \| 0.0712 \| 10.09 \| 55800 \| 0.1650 \| 1.0233 \| \| 0.0712 \| 10.11 \| 55900 \| 0.1631 \| 1.0061 \| \| 0.0667 \| 10.13 \| 56000 \| 0.1637 \| 1.0226 \| \| 0.0667 \| 10.15 \| 56100 \| 0.1607 \| 1.0042 \| \| 0.0667 \| 10.17 \| 56200 \| 0.1599 \| 1.0117 \| \| 0.0667 \| 10.18 \| 56300 \| 0.1623 \| 1.0246 \| \| 0.0667 \| 10.2 \| 56400 \| 0.1639 \| 1.0294 \| \| 0.0695 \| 10.22 \| 56500 \| 0.1650 \| 1.0232 \| \| 0.0695 \| 10.24 \| 56600 \| 0.1620 \| 1.0289 \| \| 0.0695 \| 10.26 \| 56700 \| 0.1667 \| 1.0209 \| \| 0.0695 \| 10.27 \| 56800 \| 0.1580 \| 1.0163 \| \| 0.0695 \| 10.29 \| 56900 \| 0.1646 \| 1.0293 \| \| 0.0686 \| 10.31 \| 57000 \| 0.1636 \| 1.0106 \| \| 0.0686 \| 10.33 \| 57100 \| 0.1586 \| 1.0044 \| \| 0.0686 \| 10.35 \| 57200 \| 0.1582 \| 1.0213 \| \| 0.0686 \| 10.37 \| 57300 \| 0.1627 \| 1.0151 \| \| 0.0686 \| 10.38 \| 57400 \| 0.1619 \| 1.0248 \| \| 0.0686 \| 10.4 \| 57500 \| 0.1596 \| 1.0098 \| \| 0.0686 \| 10.42 \| 57600 \| 0.1606 \| 1.0031 \| \| 0.0686 \| 10.44 \| 57700 \| 0.1620 \| 1.0046 \| \| 0.0686 \| 10.46 \| 57800 \| 0.1592 \| 1.0018 \| \| 0.0686 \| 10.47 \| 57900 \| 0.1592 \| 1.0058 \| \| 0.0669 \| 10.49 \| 58000 \| 0.1605 \| 0.9961 \| \| 0.0669 \| 10.51 \| 58100 \| 0.1632 \| 1.0102 \| \| 0.0669 \| 10.53 \| 58200 \| 0.1593 \| 1.0061 \| \| 0.0669 \| 10.55 \| 58300 \| 0.1586 \| 1.0091 \| \| 0.0669 \| 10.56 \| 58400 \| 0.1603 \| 1.0085 \| \| 0.068 \| 10.58 \| 58500 \| 0.1579 \| 1.0031 \| \| 0.068 \| 10.6 \| 58600 \| 0.1591 \| 1.0021 \| \| 0.068 \| 10.62 \| 58700 \| 0.1590 \| 1.0163 \| \| 0.068 \| 10.64 \| 58800 \| 0.1584 \| 1.0045 \| \| 0.068 \| 10.65 \| 58900 \| 0.1594 \| 1.0158 \| \| 0.0693 \| 10.67 \| 59000 \| 0.1568 \| 1.0052 \| \| 0.0693 \| 10.69 \| 59100 \| 0.1581 \| 0.9955 \| \| 0.0693 \| 10.71 \| 59200 \| 0.1622 \| 0.9917 \| \| 0.0693 \| 10.73 \| 59300 \| 0.1580 \| 1.0018 \| \| 0.0693 \| 10.75 \| 59400 \| 0.1601 \| 1.0077 \| \| 0.0699 \| 10.76 \| 59500 \| 0.1605 \| 0.9997 \| \| 0.0699 \| 10.78 \| 59600 \| 0.1585 \| 1.0009 \| \| 0.0699 \| 10.8 \| 59700 \| 0.1541 \| 1.0058 \| \| 0.0699 \| 10.82 \| 59800 \| 0.1583 \| 1.0026 \| \| 0.0699 \| 10.84 \| 59900 \| 0.1592 \| 0.9992 \| \| 0.0671 \| 10.85 \| 60000 \| 0.1590 \| 1.0004 \| \| 0.0671 \| 10.87 \| 60100 \| 0.1585 \| 1.0060 \| \| 0.0671 \| 10.89 \| 60200 \| 0.1579 \| 1.0063 \| \| 0.0671 \| 10.91 \| 60300 \| 0.1582 \| 0.9949 \| \| 0.0671 \| 10.93 \| 60400 \| 0.1562 \| 1.0004 \| \| 0.0661 \| 10.94 \| 60500 \| 0.1560 \| 0.9950 \| \| 0.0661 \| 10.96 \| 60600 \| 0.1564 \| 0.9990 \| \| 0.0661 \| 10.98 \| 60700 \| 0.1552 \| 0.9982 \| \| 0.0661 \| 11.0 \| 60800 \| 0.1596 \| 1.0018 \| \| 0.0661 \| 11.02 \| 60900 \| 0.1618 \| 0.9905 \| \| 0.0634 \| 11.03 \| 61000 \| 0.1652 \| 0.9890 \| \| 0.0634 \| 11.05 \| 61100 \| 0.1649 \| 0.9886 \| \| 0.0634 \| 11.07 \| 61200 \| 0.1668 \| 0.9870 \| \| 0.0634 \| 11.09 \| 61300 \| 0.1663 \| 0.9921 \| \| 0.0634 \| 11.11 \| 61400 \| 0.1650 \| 0.9919 \| \| 0.0587 \| 11.13 \| 61500 \| 0.1674 \| 0.9831 \| \| 0.0587 \| 11.14 \| 61600 \| 0.1633 \| 0.9793 \| \| 0.0587 \| 11.16 \| 61700 \| 0.1665 \| 0.9781 \| \| 0.0587 \| 11.18 \| 61800 \| 0.1642 \| 0.9821 \| \| 0.0587 \| 11.2 \| 61900 \| 0.1638 \| 0.9797 \| \| 0.0581 \| 11.22 \| 62000 \| 0.1628 \| 0.9727 \| \| 0.0581 \| 11.23 \| 62100 \| 0.1661 \| 0.9796 \| \| 0.0581 \| 11.25 \| 62200 \| 0.1641 \| 0.9830 \| \| 0.0581 \| 11.27 \| 62300 \| 0.1601 \| 0.9867 \| \| 0.0581 \| 11.29 \| 62400 \| 0.1626 \| 0.9757 \| \| 0.0584 \| 11.31 \| 62500 \| 0.1632 \| 1.0014 \| \| 0.0584 \| 11.32 \| 62600 \| 0.1626 \| 1.0052 \| \| 0.0584 \| 11.34 \| 62700 \| 0.1586 \| 1.0098 \| \| 0.0584 \| 11.36 \| 62800 \| 0.1597 \| 1.0151 \| \| 0.0584 \| 11.38 \| 62900 \| 0.1624 \| 1.0054 \| \| 0.0589 \| 11.4 \| 63000 \| 0.1618 \| 1.0018 \| \| 0.0589 \| 11.41 \| 63100 \| 0.1635 \| 1.0032 \| \| 0.0589 \| 11.43 \| 63200 \| 0.1654 \| 1.0142 \| \| 0.0589 \| 11.45 \| 63300 \| 0.1646 \| 1.0031 \| \| 0.0589 \| 11.47 \| 63400 \| 0.1618 \| 1.0118 \| \| 0.0579 \| 11.49 \| 63500 \| 0.1634 \| 1.0218 \| \| 0.0579 \| 11.51 \| 63600 \| 0.1616 \| 1.0179 \| \| 0.0579 \| 11.52 \| 63700 \| 0.1603 \| 1.0036 \| \| 0.0579 \| 11.54 \| 63800 \| 0.1610 \| 1.0150 \| \| 0.0579 \| 11.56 \| 63900 \| 0.1605 \| 1.0285 \| \| 0.0572 \| 11.58 \| 64000 \| 0.1621 \| 1.0261 \| \| 0.0572 \| 11.6 \| 64100 \| 0.1625 \| 1.0252 \| \| 0.0572 \| 11.61 \| 64200 \| 0.1677 \| 1.0257 \| \| 0.0572 \| 11.63 \| 64300 \| 0.1656 \| 1.0243 \| \| 0.0572 \| 11.65 \| 64400 \| 0.1669 \| 1.0270 \| \| 0.0592 \| 11.67 \| 64500 \| 0.1605 \| 1.0305 \| \| 0.0592 \| 11.69 \| 64600 \| 0.1633 \| 1.0277 \| \| 0.0592 \| 11.7 \| 64700 \| 0.1606 \| 1.0176 \| \| 0.0592 \| 11.72 \| 64800 \| 0.1618 \| 1.0249 \| \| 0.0592 \| 11.74 \| 64900 \| 0.1609 \| 1.0113 \| \| 0.0595 \| 11.76 \| 65000 \| 0.1609 \| 1.0254 \| \| 0.0595 \| 11.78 \| 65100 \| 0.1662 \| 1.0275 \| \| 0.0595 \| 11.79 \| 65200 \| 0.1652 \| 1.0164 \| \| 0.0595 \| 11.81 \| 65300 \| 0.1638 \| 1.0266 \| \| 0.0595 \| 11.83 \| 65400 \| 0.1589 \| 1.0274 \| \| 0.0588 \| 11.85 \| 65500 \| 0.1607 \| 1.0136 \| \| 0.0588 \| 11.87 \| 65600 \| 0.1592 \| 1.0136 \| \| 0.0588 \| 11.88 \| 65700 \| 0.1581 \| 1.0183 \| \| 0.0588 \| 11.9 \| 65800 \| 0.1587 \| 1.0133 \| \| 0.0588 \| 11.92 \| 65900 \| 0.1596 \| 1.0170 \| \| 0.0558 \| 11.94 \| 66000 \| 0.1590 \| 1.0161 \| \| 0.0558 \| 11.96 \| 66100 \| 0.1597 \| 1.0193 \| \| 0.0558 \| 11.98 \| 66200 \| 0.1590 \| 1.0193 \| \| 0.0558 \| 11.99 \| 66300 \| 0.1608 \| 1.0242 \| \| 0.0558 \| 12.01 \| 66400 \| 0.1642 \| 1.0231 \| \| 0.0555 \| 12.03 \| 66500 \| 0.1679 \| 1.0168 \| \| 0.0555 \| 12.05 \| 66600 \| 0.1674 \| 1.0083 \| \| 0.0555 \| 12.07 \| 66700 \| 0.1658 \| 1.0069 \| \| 0.0555 \| 12.08 \| 66800 \| 0.1661 \| 1.0134 \| \| 0.0555 \| 12.1 \| 66900 \| 0.1682 \| 1.0274 \| \| 0.0508 \| 12.12 \| 67000 \| 0.1702 \| 1.0219 \| \| 0.0508 \| 12.14 \| 67100 \| 0.1694 \| 1.0219 \| \| 0.0508 \| 12.16 \| 67200 \| 0.1667 \| 1.0236 \| \| 0.0508 \| 12.17 \| 67300 \| 0.1672 \| 1.0253 \| \| 0.0508 \| 12.19 \| 67400 \| 0.1640 \| 1.0215 \| \| 0.0513 \| 12.21 \| 67500 \| 0.1649 \| 1.0242 \| \| 0.0513 \| 12.23 \| 67600 \| 0.1687 \| 1.0262 \| \| 0.0513 \| 12.25 \| 67700 \| 0.1655 \| 1.0231 \| \| 0.0513 \| 12.26 \| 67800 \| 0.1692 \| 1.0176 \| \| 0.0513 \| 12.28 \| 67900 \| 0.1675 \| 1.0202 \| \| 0.0519 \| 12.3 \| 68000 \| 0.1644 \| 1.0241 \| \| 0.0519 \| 12.32 \| 68100 \| 0.1651 \| 1.0297 \| \| 0.0519 \| 12.34 \| 68200 \| 0.1661 \| 1.0287 \| \| 0.0519 \| 12.36 \| 68300 \| 0.1665 \| 1.0257 \| \| 0.0519 \| 12.37 \| 68400 \| 0.1685 \| 1.0233 \| \| 0.0522 \| 12.39 \| 68500 \| 0.1636 \| 1.0177 \| \| 0.0522 \| 12.41 \| 68600 \| 0.1709 \| 1.0200 \| \| 0.0522 \| 12.43 \| 68700 \| 0.1684 \| 1.0164 \| \| 0.0522 \| 12.45 \| 68800 \| 0.1666 \| 1.0119 \| \| 0.0522 \| 12.46 \| 68900 \| 0.1683 \| 1.0136 \| \| 0.05 \| 12.48 \| 69000 \| 0.1696 \| 1.0127 \| \| 0.05 \| 12.5 \| 69100 \| 0.1708 \| 1.0184 \| \| 0.05 \| 12.52 \| 69200 \| 0.1654 \| 1.0282 \| \| 0.05 \| 12.54 \| 69300 \| 0.1700 \| 1.0235 \| \| 0.05 \| 12.55 \| 69400 \| 0.1688 \| 1.0257 \| \| 0.0513 \| 12.57 \| 69500 \| 0.1646 \| 1.0274 \| \| 0.0513 \| 12.59 \| 69600 \| 0.1660 \| 1.0247 \| \| 0.0513 \| 12.61 \| 69700 \| 0.1657 \| 1.0188 \| \| 0.0513 \| 12.63 \| 69800 \| 0.1654 \| 1.0087 \| \| 0.0513 \| 12.64 \| 69900 \| 0.1681 \| 1.0146 \| \| 0.0512 \| 12.66 \| 70000 \| 0.1660 \| 1.0185 \| \| 0.0512 \| 12.68 \| 70100 \| 0.1690 \| 1.0214 \| \| 0.0512 \| 12.7 \| 70200 \| 0.1683 \| 1.0160 \| \| 0.0512 \| 12.72 \| 70300 \| 0.1695 \| 1.0198 \| \| 0.0512 \| 12.74 \| 70400 \| 0.1666 \| 1.0193 \| \| 0.0484 \| 12.75 \| 70500 \| 0.1654 \| 1.0142 \| \| 0.0484 \| 12.77 \| 70600 \| 0.1598 \| 1.0154 \| \| 0.0484 \| 12.79 \| 70700 \| 0.1623 \| 1.0139 \| \| 0.0484 \| 12.81 \| 70800 \| 0.1662 \| 1.0180 \| \| 0.0484 \| 12.83 \| 70900 \| 0.1659 \| 1.0232 \| \| 0.0501 \| 12.84 \| 71000 \| 0.1662 \| 1.0202 \| \| 0.0501 \| 12.86 \| 71100 \| 0.1639 \| 1.0161 \| \| 0.0501 \| 12.88 \| 71200 \| 0.1666 \| 1.0151 \| \| 0.0501 \| 12.9 \| 71300 \| 0.1644 \| 1.0129 \| \| 0.0501 \| 12.92 \| 71400 \| 0.1642 \| 1.0171 \| \| 0.0482 \| 12.93 \| 71500 \| 0.1635 \| 1.0162 \| \| 0.0482 \| 12.95 \| 71600 \| 0.1637 \| 1.0186 \| \| 0.0482 \| 12.97 \| 71700 \| 0.1639 \| 1.0142 \| \| 0.0482 \| 12.99 \| 71800 \| 0.1643 \| 1.0122 \| \| 0.0482 \| 13.01 \| 71900 \| 0.1679 \| 1.0156 \| \| 0.0483 \| 13.02 \| 72000 \| 0.1717 \| 1.0224 \| \| 0.0483 \| 13.04 \| 72100 \| 0.1742 \| 1.0229 \| \| 0.0483 \| 13.06 \| 72200 \| 0.1718 \| 1.0237 \| \| 0.0483 \| 13.08 \| 72300 \| 0.1742 \| 1.0266 \| \| 0.0483 \| 13.1 \| 72400 \| 0.1736 \| 1.0257 \| \| 0.0443 \| 13.12 \| 72500 \| 0.1741 \| 1.0275 \| \| 0.0443 \| 13.13 \| 72600 \| 0.1745 \| 1.0325 \| \| 0.0443 \| 13.15 \| 72700 \| 0.1737 \| 1.0296 \| \| 0.0443 \| 13.17 \| 72800 \| 0.1722 \| 1.0303 \| \| 0.0443 \| 13.19 \| 72900 \| 0.1702 \| 1.0305 \| \| 0.0424 \| 13.21 \| 73000 \| 0.1733 \| 1.0241 \| \| 0.0424 \| 13.22 \| 73100 \| 0.1748 \| 1.0243 \| \| 0.0424 \| 13.24 \| 73200 \| 0.1760 \| 1.0231 \| \| 0.0424 \| 13.26 \| 73300 \| 0.1745 \| 1.0241 \| \| 0.0424 \| 13.28 \| 73400 \| 0.1772 \| 1.0217 \| \| 0.0424 \| 13.3 \| 73500 \| 0.1755 \| 1.0206 \| \| 0.0424 \| 13.31 \| 73600 \| 0.1743 \| 1.0242 \| \| 0.0424 \| 13.33 \| 73700 \| 0.1738 \| 1.0208 \| \| 0.0424 \| 13.35 \| 73800 \| 0.1736 \| 1.0249 \| \| 0.0424 \| 13.37 \| 73900 \| 0.1747 \| 1.0271 \| \| 0.0437 \| 13.39 \| 74000 \| 0.1707 \| 1.0241 \| \| 0.0437 \| 13.4 \| 74100 \| 0.1731 \| 1.0269 \| \| 0.0437 \| 13.42 \| 74200 \| 0.1743 \| 1.0290 \| \| 0.0437 \| 13.44 \| 74300 \| 0.1739 \| 1.0266 \| \| 0.0437 \| 13.46 \| 74400 \| 0.1763 \| 1.0246 \| \| 0.0443 \| 13.48 \| 74500 \| 0.1724 \| 1.0209 \| \| 0.0443 \| 13.49 \| 74600 \| 0.1744 \| 1.0244 \| \| 0.0443 \| 13.51 \| 74700 \| 0.1717 \| 1.0232 \| \| 0.0443 \| 13.53 \| 74800 \| 0.1754 \| 1.0217 \| \| 0.0443 \| 13.55 \| 74900 \| 0.1721 \| 1.0234 \| \| 0.0435 \| 13.57 \| 75000 \| 0.1751 \| 1.0197 \| \| 0.0435 \| 13.59 \| 75100 \| 0.1727 \| 1.0285 \| \| 0.0435 \| 13.6 \| 75200 \| 0.1715 \| 1.0221 \| \| 0.0435 \| 13.62 \| 75300 \| 0.1746 \| 1.0247 \| \| 0.0435 \| 13.64 \| 75400 \| 0.1712 \| 1.0231 \| \| 0.0436 \| 13.66 \| 75500 \| 0.1719 \| 1.0228 \| \| 0.0436 \| 13.68 \| 75600 \| 0.1727 \| 1.0197 \| \| 0.0436 \| 13.69 \| 75700 \| 0.1750 \| 1.0252 \| \| 0.0436 \| 13.71 \| 75800 \| 0.1702 \| 1.0241 \| \| 0.0436 \| 13.73 \| 75900 \| 0.1720 \| 1.0250 \| \| 0.0433 \| 13.75 \| 76000 \| 0.1744 \| 1.0210 \| \| 0.0433 \| 13.77 \| 76100 \| 0.1735 \| 1.0211 \| \| 0.0433 \| 13.78 \| 76200 \| 0.1727 \| 1.0205 \| \| 0.0433 \| 13.8 \| 76300 \| 0.1706 \| 1.0218 \| \| 0.0433 \| 13.82 \| 76400 \| 0.1709 \| 1.0238 \| \| 0.0431 \| 13.84 \| 76500 \| 0.1705 \| 1.0197 \| \| 0.0431 \| 13.86 \| 76600 \| 0.1734 \| 1.0223 \| \| 0.0431 \| 13.87 \| 76700 \| 0.1695 \| 1.0250 \| \| 0.0431 \| 13.89 \| 76800 \| 0.1734 \| 1.0232 \| \| 0.0431 \| 13.91 \| 76900 \| 0.1724 \| 1.0219 \| \| 0.041 \| 13.93 \| 77000 \| 0.1706 \| 1.0236 \| \| 0.041 \| 13.95 \| 77100 \| 0.1689 \| 1.0220 \| \| 0.041 \| 13.97 \| 77200 \| 0.1738 \| 1.0230 \| \| 0.041 \| 13.98 \| 77300 \| 0.1727 \| 1.0254 \| \| 0.041 \| 14.0 \| 77400 \| 0.1721 \| 1.0261 \| \| 0.041 \| 14.02 \| 77500 \| 0.1760 \| 1.0261 \| \| 0.041 \| 14.04 \| 77600 \| 0.1772 \| 1.0202 \| \| 0.041 \| 14.06 \| 77700 \| 0.1782 \| 1.0202 \| \| 0.041 \| 14.07 \| 77800 \| 0.1777 \| 1.0222 \| \| 0.041 \| 14.09 \| 77900 \| 0.1787 \| 1.0203 \| \| 0.0383 \| 14.11 \| 78000 \| 0.1790 \| 1.0236 \| \| 0.0383 \| 14.13 \| 78100 \| 0.1812 \| 1.0245 \| \| 0.0383 \| 14.15 \| 78200 \| 0.1778 \| 1.0224 \| \| 0.0383 \| 14.16 \| 78300 \| 0.1771 \| 1.0231 \| \| 0.0383 \| 14.18 \| 78400 \| 0.1782 \| 1.0242 \| \| 0.0391 \| 14.2 \| 78500 \| 0.1785 \| 1.0262 \| \| 0.0391 \| 14.22 \| 78600 \| 0.1791 \| 1.0261 \| \| 0.0391 \| 14.24 \| 78700 \| 0.1770 \| 1.0254 \| \| 0.0391 \| 14.25 \| 78800 \| 0.1810 \| 1.0257 \| \| 0.0391 \| 14.27 \| 78900 \| 0.1794 \| 1.0241 \| \| 0.0387 \| 14.29 \| 79000 \| 0.1774 \| 1.0256 \| \| 0.0387 \| 14.31 \| 79100 \| 0.1774 \| 1.0236 \| \| 0.0387 \| 14.33 \| 79200 \| 0.1759 \| 1.0222 \| \| 0.0387 \| 14.35 \| 79300 \| 0.1787 \| 1.0237 \| \| 0.0387 \| 14.36 \| 79400 \| 0.1788 \| 1.0227 \| \| 0.0372 \| 14.38 \| 79500 \| 0.1789 \| 1.0232 \| \| 0.0372 \| 14.4 \| 79600 \| 0.1771 \| 1.0254 \| \| 0.0372 \| 14.42 \| 79700 \| 0.1777 \| 1.0244 \| \| 0.0372 \| 14.44 \| 79800 \| 0.1791 \| 1.0225 \| \| 0.0372 \| 14.45 \| 79900 \| 0.1786 \| 1.0237 \| \| 0.0385 \| 14.47 \| 80000 \| 0.1782 \| 1.0243 \| \| 0.0385 \| 14.49 \| 80100 \| 0.1770 \| 1.0236 \| \| 0.0385 \| 14.51 \| 80200 \| 0.1782 \| 1.0240 \| \| 0.0385 \| 14.53 \| 80300 \| 0.1764 \| 1.0243 \| \| 0.0385 \| 14.54 \| 80400 \| 0.1748 \| 1.0248 \| \| 0.039 \| 14.56 \| 80500 \| 0.1758 \| 1.0232 \| \| 0.039 \| 14.58 \| 80600 \| 0.1763 \| 1.0246 \| \| 0.039 \| 14.6 \| 80700 \| 0.1770 \| 1.0220 \| \| 0.039 \| 14.62 \| 80800 \| 0.1788 \| 1.0225 \| \| 0.039 \| 14.63 \| 80900 \| 0.1781 \| 1.0230 \| \| 0.039 \| 14.65 \| 81000 \| 0.1779 \| 1.0230 \| \| 0.039 \| 14.67 \| 81100 \| 0.1755 \| 1.0212 \| \| 0.039 \| 14.69 \| 81200 \| 0.1765 \| 1.0226 \| \| 0.039 \| 14.71 \| 81300 \| 0.1787 \| 1.0241 \| \| 0.039 \| 14.72 \| 81400 \| 0.1782 \| 1.0250 \| \| 0.0368 \| 14.74 \| 81500 \| 0.1780 \| 1.0248 \| \| 0.0368 \| 14.76 \| 81600 \| 0.1782 \| 1.0242 \| \| 0.0368 \| 14.78 \| 81700 \| 0.1782 \| 1.0242 \| \| 0.0368 \| 14.8 \| 81800 \| 0.1792 \| 1.0241 \| \| 0.0368 \| 14.82 \| 81900 \| 0.1796 \| 1.0238 \| \| 0.0378 \| 14.83 \| 82000 \| 0.1795 \| 1.0236 \| \| 0.0378 \| 14.85 \| 82100 \| 0.1796 \| 1.0239 \| \| 0.0378 \| 14.87 \| 82200 \| 0.1792 \| 1.0236 \| \| 0.0378 \| 14.89 \| 82300 \| 0.1789 \| 1.0239 \| \| 0.0378 \| 14.91 \| 82400 \| 0.1788 \| 1.0238 \| \| 0.0386 \| 14.92 \| 82500 \| 0.1787 \| 1.0239 \| \| 0.0386 \| 14.94 \| 82600 \| 0.1786 \| 1.0236 \| \| 0.0386 \| 14.96 \| 82700 \| 0.1786 \| 1.0237 \| \| 0.0386 \| 14.98 \| 82800 \| 0.1787 \| 1.0239 \| \| 0.0386 \| 15.0 \| 82900 \| 0.1788 \| 1.0238 \| ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1 - Datasets 1.17.0 - Tokenizers 0.10.3
CLAck/en-km	[ "pytorch", "marian", "text2text-generation", "transformers", "translation", "autotrain_compatible" ]	translation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	12	null	--- tags: - conversational - tagalog - filipino language: - tl --- # Tagalog DialoGPT This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium). This model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
CLAck/en-vi	[ "pytorch", "marian", "text2text-generation", "en", "vi", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]	translation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	null	--- tags: - conversational - tagalog - filipino inference: false language: - tl --- # Tagalog DialoGPT This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium). This model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
CLAck/indo-mixed	[ "pytorch", "marian", "text2text-generation", "en", "id", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]	translation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	15	null	--- tags: - conversational - tagalog - filipino inference: false language: - tl --- # Tagalog DialoGPT This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium). This model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens.
CLAck/indo-pure	[ "pytorch", "marian", "text2text-generation", "en", "id", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]	translation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	4	null	--- tags: - conversational - tagalog - filipino language: - tl inference: false datasets: - gabtan99/pex-conversations --- # Tagalog DialoGPT A DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training. # Latest release: July 25, 2021 * The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset. # Dataset [PEx Conversations Dataset](https://huggingface.co/datasets/gabtan99/pex-conversations) # Usage Here is an example of using beam search for model inference. ``` for step in range(2): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # we limit the generation to 512 tokens, each utterance in training had a maximum of 128 tokens chat_history_ids = model.generate( bot_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id, num_beams=5, no_repeat_ngram_size=3 ) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ``` # Training Script [Fine-tuning script adapted from Spanish DialoGPT](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb) # Research by * [tyadrianpaule](https://huggingface.co/tyadrianpaule) * [schuylerng](https://huggingface.co/schuylerng) * [dcl127](https://huggingface.co/dcl127)
CLAck/vi-en	[ "pytorch", "marian", "text2text-generation", "en", "vi", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]	translation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	6	null	I am adding my first README in order to test the interface. How good is it really?
CLEE/CLEE	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 --- This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```
CLTL/MedRoBERTa.nl	[ "pytorch", "roberta", "fill-mask", "nl", "transformers", "license:mit", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	2,988	null	--- license: apache-2.0 --- This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```
CLTL/gm-ner-xlmrbase	[ "pytorch", "tf", "xlm-roberta", "token-classification", "nl", "transformers", "dighum", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "XLMRobertaForTokenClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	2	null	--- license: apache-2.0 --- This model is used in the paper Generative Relation Linking for Question Answering over Knowledge Bases. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```
Cameron/BERT-SBIC-offensive	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	31	null	# Leetcode using AI :robot: GPT-2 Model for Leetcode Questions in python Note: the Answers might not make sense in some cases because of the bias in GPT-2 Contribtuions: If you would like to make the model better contributions are welcome Check out [CONTRIBUTIONS.md](https://github.com/gagan3012/project-code-py/blob/master/CONTRIBUTIONS.md) ### 📢 Favour: It would be highly motivating, if you can STAR⭐ this repo if you find it helpful. ## Model Two models have been developed for different use cases and they can be found at https://huggingface.co/gagan3012 The model weights can be found here: [GPT-2](https://huggingface.co/gagan3012/project-code-py) and [DistilGPT-2](https://huggingface.co/gagan3012/project-code-py-small) ### Example usage: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("gagan3012/project-code-py") model = AutoModelWithLMHead.from_pretrained("gagan3012/project-code-py") ``` ## Demo [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/gagan3012/project-code-py/app.py) A streamlit webapp has been setup to use the model: https://share.streamlit.io/gagan3012/project-code-py/app.py ![image](https://user-images.githubusercontent.com/49101362/110356042-e69e4780-804a-11eb-94c6-a47fa3cd66b3.png) ## Example results: ### Question: ``` Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list. ``` ### Answer: ```python """ Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list. For example, a = 1->2->3 b = 3->1->2 t = ListNode(-1, 1) Note: The lexicographic ordering of the nodes in a tree matters. Do not assign values to nodes in a tree. Example 1: Input: [1,2,3] Output: 1->2->5 Explanation: 1->2->3->3->4, then 1->2->5[2] and then 5->1->3->4. Note: The length of a linked list will be in the range [1, 1000]. Node.val must be a valid LinkedListNode type. Both the length and the value of the nodes in a linked list will be in the range [-1000, 1000]. All nodes are distinct. """ # Definition for singly-linked list. # class ListNode: # def __init__(self, x): # self.val = x # self.next = None class Solution: def deleteNode(self, head: ListNode, val: int) -> None: """ BFS Linked List :param head: ListNode :param val: int :return: ListNode """ if head is not None: return head dummy = ListNode(-1, 1) dummy.next = head dummy.next.val = val dummy.next.next = head dummy.val = "" s1 = Solution() print(s1.deleteNode(head)) print(s1.deleteNode(-1)) print(s1.deleteNode(-1)) ```
Capreolus/bert-base-msmarco	[ "pytorch", "tf", "jax", "bert", "text-classification", "arxiv:2008.09093", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	238	null	--- language: - hi-en tags: - sentiment - multilingual - hindi codemix - hinglish license: apache-2.0 datasets: - sail --- # Sentiment Classification for hinglish text: `gk-hinglish-sentiment` ## Model description Trained small amount of reviews dataset ## Intended uses & limitations I wanted something to work well with hinglish data as it is being used in India mostly. The training data was not much as expected #### How to use ```python #sample code from transformers import BertTokenizer, BertForSequenceClassification tokenizerg = BertTokenizer.from_pretrained("/content/model") modelg = BertForSequenceClassification.from_pretrained("/content/model") text = "kuch bhi type karo hinglish mai" encoded_input = tokenizerg(text, return_tensors='pt') output = modelg(**encoded_input) print(output) #output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive ``` #### Limitations and bias The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data ## Training data Training data contains labeled data for 3 labels link to the pre-trained model card with description of the pre-training data. I have Tuned below model https://huggingface.co/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment ### BibTeX entry and citation info ```@inproceedings{khanuja-etal-2020-gluecos, title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}", author = "Khanuja, Simran and Dandapat, Sandipan and Srinivasan, Anirudh and Sitaram, Sunayana and Choudhury, Monojit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.329", pages = "3575--3585" } ```
dccuchile/albert-base-spanish-finetuned-xnli	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	28	null	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # gaussfer/test_simcse_new This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('gaussfer/test_simcse_new') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('gaussfer/test_simcse_new') model = AutoModel.from_pretrained('gaussfer/test_simcse_new') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=gaussfer/test_simcse_new) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 875 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 40, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
dccuchile/albert-large-spanish-finetuned-pos	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-finetuned-pubmed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-finetuned-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5363 - Rouge2 Precision: 0.3459 - Rouge2 Recall: 0.2455 - Rouge2 Fmeasure: 0.2731 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.652 \| 1.0 \| 1125 \| 1.5087 \| 0.3647 \| 0.2425 \| 0.2772 \| \| 1.4695 \| 2.0 \| 2250 \| 1.5039 \| 0.3448 \| 0.2457 \| 0.2732 \| \| 1.3714 \| 3.0 \| 3375 \| 1.4842 \| 0.3509 \| 0.2474 \| 0.277 \| \| 1.2734 \| 4.0 \| 4500 \| 1.4901 \| 0.3452 \| 0.2426 \| 0.2716 \| \| 1.1853 \| 5.0 \| 5625 \| 1.5152 \| 0.3658 \| 0.2371 \| 0.2744 \| \| 1.0975 \| 6.0 \| 6750 \| 1.5133 \| 0.3529 \| 0.2417 \| 0.2729 \| \| 1.0448 \| 7.0 \| 7875 \| 1.5203 \| 0.3485 \| 0.2464 \| 0.275 \| \| 0.9999 \| 8.0 \| 9000 \| 1.5316 \| 0.3437 \| 0.2435 \| 0.2719 \| \| 0.9732 \| 9.0 \| 10125 \| 1.5338 \| 0.3464 \| 0.2446 \| 0.2732 \| \| 0.954 \| 10.0 \| 11250 \| 1.5363 \| 0.3459 \| 0.2455 \| 0.2731 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-large-spanish-finetuned-qa-mlqa	[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-mlm-pubmed-15 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4822 - Rouge2 Precision: 0.7578 - Rouge2 Recall: 0.5933 - Rouge2 Fmeasure: 0.6511 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.7006 \| 1.0 \| 663 \| 0.5062 \| 0.7492 \| 0.5855 \| 0.6434 \| \| 0.5709 \| 2.0 \| 1326 \| 0.4811 \| 0.7487 \| 0.5879 \| 0.6447 \| \| 0.5011 \| 3.0 \| 1989 \| 0.4734 \| 0.7541 \| 0.5906 \| 0.6483 \| \| 0.4164 \| 4.0 \| 2652 \| 0.4705 \| 0.7515 \| 0.5876 \| 0.6452 \| \| 0.3888 \| 5.0 \| 3315 \| 0.4703 \| 0.7555 \| 0.5946 \| 0.6515 \| \| 0.3655 \| 6.0 \| 3978 \| 0.4725 \| 0.7572 \| 0.5943 \| 0.6516 \| \| 0.319 \| 7.0 \| 4641 \| 0.4733 \| 0.7557 \| 0.5911 \| 0.6491 \| \| 0.3089 \| 8.0 \| 5304 \| 0.4792 \| 0.7577 \| 0.5936 \| 0.6513 \| \| 0.2907 \| 9.0 \| 5967 \| 0.4799 \| 0.7577 \| 0.5931 \| 0.6509 \| \| 0.275 \| 10.0 \| 6630 \| 0.4822 \| 0.7578 \| 0.5933 \| 0.6511 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-tiny-spanish-finetuned-mldoc	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	32	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-35 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-mlm-pubmed-35 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9359 - Rouge2 Precision: 0.5451 - Rouge2 Recall: 0.4232 - Rouge2 Fmeasure: 0.4666 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.4156 \| 1.0 \| 663 \| 1.0366 \| 0.5165 \| 0.3967 \| 0.4394 \| \| 1.1773 \| 2.0 \| 1326 \| 0.9841 \| 0.5354 \| 0.4168 \| 0.4589 \| \| 1.0894 \| 3.0 \| 1989 \| 0.9554 \| 0.5346 \| 0.4133 \| 0.4563 \| \| 0.9359 \| 4.0 \| 2652 \| 0.9440 \| 0.5357 \| 0.4163 \| 0.4587 \| \| 0.8758 \| 5.0 \| 3315 \| 0.9340 \| 0.5428 \| 0.4226 \| 0.465 \| \| 0.8549 \| 6.0 \| 3978 \| 0.9337 \| 0.5385 \| 0.422 \| 0.4634 \| \| 0.7743 \| 7.0 \| 4641 \| 0.9330 \| 0.542 \| 0.422 \| 0.4647 \| \| 0.7465 \| 8.0 \| 5304 \| 0.9315 \| 0.5428 \| 0.4231 \| 0.4654 \| \| 0.7348 \| 9.0 \| 5967 \| 0.9344 \| 0.5462 \| 0.4244 \| 0.4674 \| \| 0.7062 \| 10.0 \| 6630 \| 0.9359 \| 0.5451 \| 0.4232 \| 0.4666 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-tiny-spanish-finetuned-ner	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-45 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-mlm-pubmed-45 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1797 - Rouge2 Precision: 0.4333 - Rouge2 Recall: 0.3331 - Rouge2 Fmeasure: 0.3684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.7989 \| 1.0 \| 663 \| 1.3385 \| 0.4097 \| 0.3086 \| 0.3444 \| \| 1.5072 \| 2.0 \| 1326 \| 1.2582 \| 0.4218 \| 0.3213 \| 0.3569 \| \| 1.4023 \| 3.0 \| 1989 \| 1.2236 \| 0.4207 \| 0.3211 \| 0.3562 \| \| 1.2205 \| 4.0 \| 2652 \| 1.2025 \| 0.4359 \| 0.3331 \| 0.3696 \| \| 1.1584 \| 5.0 \| 3315 \| 1.1910 \| 0.4304 \| 0.3307 \| 0.3658 \| \| 1.1239 \| 6.0 \| 3978 \| 1.1830 \| 0.4247 \| 0.3279 \| 0.3618 \| \| 1.0384 \| 7.0 \| 4641 \| 1.1761 \| 0.4308 \| 0.3325 \| 0.367 \| \| 1.0168 \| 8.0 \| 5304 \| 1.1762 \| 0.4314 \| 0.3336 \| 0.368 \| \| 0.9966 \| 9.0 \| 5967 \| 1.1773 \| 0.4335 \| 0.3341 \| 0.369 \| \| 0.961 \| 10.0 \| 6630 \| 1.1797 \| 0.4333 \| 0.3331 \| 0.3684 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-tiny-spanish-finetuned-pawsx	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	29	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-medterm results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-mlm-pubmed-medterm This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Rouge2 Precision: 0.985 - Rouge2 Recall: 0.7208 - Rouge2 Fmeasure: 0.8088 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.0018 \| 1.0 \| 13833 \| 0.0003 \| 0.985 \| 0.7208 \| 0.8088 \| \| 0.0014 \| 2.0 \| 27666 \| 0.0006 \| 0.9848 \| 0.7207 \| 0.8086 \| \| 0.0009 \| 3.0 \| 41499 \| 0.0002 \| 0.9848 \| 0.7207 \| 0.8086 \| \| 0.0007 \| 4.0 \| 55332 \| 0.0002 \| 0.985 \| 0.7208 \| 0.8088 \| \| 0.0006 \| 5.0 \| 69165 \| 0.0001 \| 0.9848 \| 0.7207 \| 0.8087 \| \| 0.0001 \| 6.0 \| 82998 \| 0.0002 \| 0.9846 \| 0.7206 \| 0.8086 \| \| 0.0009 \| 7.0 \| 96831 \| 0.0001 \| 0.9848 \| 0.7208 \| 0.8087 \| \| 0.0 \| 8.0 \| 110664 \| 0.0000 \| 0.9848 \| 0.7207 \| 0.8087 \| \| 0.0001 \| 9.0 \| 124497 \| 0.0000 \| 0.985 \| 0.7208 \| 0.8088 \| \| 0.0 \| 10.0 \| 138330 \| 0.0000 \| 0.985 \| 0.7208 \| 0.8088 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
dccuchile/albert-tiny-spanish-finetuned-pos	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-mlm-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Rouge2 Precision: 0.6572 - Rouge2 Recall: 0.5164 - Rouge2 Fmeasure: 0.5662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.0322 \| 1.0 \| 663 \| 0.7891 \| 0.639 \| 0.4989 \| 0.5491 \| \| 0.8545 \| 2.0 \| 1326 \| 0.7433 \| 0.6461 \| 0.5057 \| 0.5556 \| \| 0.758 \| 3.0 \| 1989 \| 0.7299 \| 0.647 \| 0.5033 \| 0.5547 \| \| 0.6431 \| 4.0 \| 2652 \| 0.7185 \| 0.6556 \| 0.5101 \| 0.5616 \| \| 0.6058 \| 5.0 \| 3315 \| 0.7126 \| 0.6537 \| 0.5144 \| 0.5638 \| \| 0.5726 \| 6.0 \| 3978 \| 0.7117 \| 0.6567 \| 0.5169 \| 0.5666 \| \| 0.5168 \| 7.0 \| 4641 \| 0.7150 \| 0.6585 \| 0.5154 \| 0.566 \| \| 0.5011 \| 8.0 \| 5304 \| 0.7220 \| 0.6568 \| 0.5164 \| 0.5664 \| \| 0.4803 \| 9.0 \| 5967 \| 0.7208 \| 0.6573 \| 0.5161 \| 0.5662 \| \| 0.4577 \| 10.0 \| 6630 \| 0.7223 \| 0.6572 \| 0.5164 \| 0.5662 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-tiny-spanish-finetuned-qa-mlqa	[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-paraphrase-pubmed-1.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-paraphrase-pubmed-1.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4236 - Rouge2 Precision: 0.8482 - Rouge2 Recall: 0.673 - Rouge2 Fmeasure: 0.7347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.6534 \| 1.0 \| 663 \| 0.4641 \| 0.8448 \| 0.6691 \| 0.7313 \| \| 0.5078 \| 2.0 \| 1326 \| 0.4398 \| 0.8457 \| 0.6719 \| 0.7333 \| \| 0.4367 \| 3.0 \| 1989 \| 0.4274 \| 0.847 \| 0.6717 \| 0.7335 \| \| 0.3575 \| 4.0 \| 2652 \| 0.4149 \| 0.8481 \| 0.6733 \| 0.735 \| \| 0.3319 \| 5.0 \| 3315 \| 0.4170 \| 0.8481 \| 0.6724 \| 0.7343 \| \| 0.3179 \| 6.0 \| 3978 \| 0.4264 \| 0.8484 \| 0.6733 \| 0.735 \| \| 0.2702 \| 7.0 \| 4641 \| 0.4207 \| 0.8489 \| 0.6732 \| 0.7353 \| \| 0.2606 \| 8.0 \| 5304 \| 0.4205 \| 0.8487 \| 0.6725 \| 0.7347 \| \| 0.2496 \| 9.0 \| 5967 \| 0.4247 \| 0.8466 \| 0.6717 \| 0.7334 \| \| 0.2353 \| 10.0 \| 6630 \| 0.4236 \| 0.8482 \| 0.673 \| 0.7347 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-tiny-spanish-finetuned-xnli	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	31	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-paraphrase-pubmed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-paraphrase-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6340 - Rouge2 Precision: 0.83 - Rouge2 Recall: 0.6526 - Rouge2 Fmeasure: 0.7144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.6613 \| 1.0 \| 663 \| 0.4750 \| 0.8321 \| 0.6552 \| 0.7167 \| \| 0.4993 \| 2.0 \| 1326 \| 0.4404 \| 0.8366 \| 0.6583 \| 0.7203 \| \| 0.443 \| 3.0 \| 1989 \| 0.4261 \| 0.8319 \| 0.6562 \| 0.7176 \| \| 0.3482 \| 4.0 \| 2652 \| 0.4198 \| 0.8348 \| 0.6571 \| 0.7191 \| \| 0.3206 \| 5.0 \| 3315 \| 0.4233 \| 0.8344 \| 0.656 \| 0.7183 \| \| 0.294 \| 6.0 \| 3978 \| 0.4334 \| 0.835 \| 0.657 \| 0.719 \| \| 0.2404 \| 7.0 \| 4641 \| 0.4437 \| 0.8334 \| 0.6559 \| 0.7178 \| \| 0.2228 \| 8.0 \| 5304 \| 0.4438 \| 0.8348 \| 0.6565 \| 0.7187 \| \| 0.211 \| 9.0 \| 5967 \| 0.4516 \| 0.8329 \| 0.6549 \| 0.717 \| \| 0.1713 \| 10.0 \| 6630 \| 0.4535 \| 0.8332 \| 0.6547 \| 0.7169 \| \| 0.1591 \| 11.0 \| 7293 \| 0.4763 \| 0.8349 \| 0.6561 \| 0.7184 \| \| 0.1555 \| 12.0 \| 7956 \| 0.4824 \| 0.8311 \| 0.6534 \| 0.7153 \| \| 0.1262 \| 13.0 \| 8619 \| 0.4883 \| 0.8322 \| 0.655 \| 0.7167 \| \| 0.1164 \| 14.0 \| 9282 \| 0.5025 \| 0.8312 \| 0.6539 \| 0.7158 \| \| 0.1108 \| 15.0 \| 9945 \| 0.5149 \| 0.8321 \| 0.6535 \| 0.7157 \| \| 0.0926 \| 16.0 \| 10608 \| 0.5340 \| 0.8315 \| 0.6544 \| 0.7159 \| \| 0.0856 \| 17.0 \| 11271 \| 0.5322 \| 0.8306 \| 0.6518 \| 0.7142 \| \| 0.0785 \| 18.0 \| 11934 \| 0.5346 \| 0.8324 \| 0.6549 \| 0.7167 \| \| 0.071 \| 19.0 \| 12597 \| 0.5488 \| 0.8311 \| 0.652 \| 0.714 \| \| 0.0635 \| 20.0 \| 13260 \| 0.5624 \| 0.8287 \| 0.6517 \| 0.7132 \| \| 0.0608 \| 21.0 \| 13923 \| 0.5612 \| 0.8299 \| 0.6527 \| 0.7141 \| \| 0.0531 \| 22.0 \| 14586 \| 0.5764 \| 0.8283 \| 0.6498 \| 0.7119 \| \| 0.0486 \| 23.0 \| 15249 \| 0.5832 \| 0.8298 \| 0.6532 \| 0.7148 \| \| 0.0465 \| 24.0 \| 15912 \| 0.5866 \| 0.83 \| 0.6522 \| 0.7142 \| \| 0.0418 \| 25.0 \| 16575 \| 0.5825 \| 0.83 \| 0.6523 \| 0.7141 \| \| 0.0391 \| 26.0 \| 17238 \| 0.5997 \| 0.8306 \| 0.6545 \| 0.716 \| \| 0.0376 \| 27.0 \| 17901 \| 0.5894 \| 0.8315 \| 0.6546 \| 0.7164 \| \| 0.035 \| 28.0 \| 18564 \| 0.6045 \| 0.8306 \| 0.6529 \| 0.7149 \| \| 0.0316 \| 29.0 \| 19227 \| 0.6168 \| 0.8311 \| 0.6546 \| 0.7162 \| \| 0.0314 \| 30.0 \| 19890 \| 0.6203 \| 0.8311 \| 0.6552 \| 0.7164 \| \| 0.0292 \| 31.0 \| 20553 \| 0.6173 \| 0.8315 \| 0.6548 \| 0.7163 \| \| 0.0265 \| 32.0 \| 21216 \| 0.6226 \| 0.832 \| 0.6548 \| 0.7166 \| \| 0.0274 \| 33.0 \| 21879 \| 0.6264 \| 0.8314 \| 0.6538 \| 0.7155 \| \| 0.0247 \| 34.0 \| 22542 \| 0.6254 \| 0.8289 \| 0.6515 \| 0.7132 \| \| 0.0238 \| 35.0 \| 23205 \| 0.6254 \| 0.8307 \| 0.6519 \| 0.7142 \| \| 0.0232 \| 36.0 \| 23868 \| 0.6295 \| 0.8287 \| 0.6515 \| 0.7133 \| \| 0.0215 \| 37.0 \| 24531 \| 0.6326 \| 0.8293 \| 0.6523 \| 0.7138 \| \| 0.0212 \| 38.0 \| 25194 \| 0.6332 \| 0.8295 \| 0.6522 \| 0.714 \| \| 0.0221 \| 39.0 \| 25857 \| 0.6335 \| 0.8305 \| 0.6528 \| 0.7147 \| \| 0.0202 \| 40.0 \| 26520 \| 0.6340 \| 0.83 \| 0.6526 \| 0.7144 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-xlarge-spanish-finetuned-ner	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-pubmed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6131 - Rouge2 Precision: 0.3 - Rouge2 Recall: 0.2152 - Rouge2 Fmeasure: 0.2379 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 2.1335 \| 1.0 \| 563 \| 1.7632 \| 0.2716 \| 0.1936 \| 0.2135 \| \| 1.9373 \| 2.0 \| 1126 \| 1.7037 \| 0.2839 \| 0.2068 \| 0.2265 \| \| 1.8827 \| 3.0 \| 1689 \| 1.6723 \| 0.2901 \| 0.2118 \| 0.2316 \| \| 1.8257 \| 4.0 \| 2252 \| 1.6503 \| 0.2938 \| 0.2115 \| 0.2332 \| \| 1.8152 \| 5.0 \| 2815 \| 1.6386 \| 0.2962 \| 0.2139 \| 0.2357 \| \| 1.7939 \| 6.0 \| 3378 \| 1.6284 \| 0.2976 \| 0.212 \| 0.2354 \| \| 1.7845 \| 7.0 \| 3941 \| 1.6211 \| 0.2991 \| 0.2155 \| 0.2383 \| \| 1.7468 \| 8.0 \| 4504 \| 1.6167 \| 0.2994 \| 0.217 \| 0.239 \| \| 1.7464 \| 9.0 \| 5067 \| 1.6137 \| 0.3007 \| 0.2154 \| 0.2382 \| \| 1.744 \| 10.0 \| 5630 \| 1.6131 \| 0.3 \| 0.2152 \| 0.2379 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-xlarge-spanish-finetuned-pawsx	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	24	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed-15 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-mlm-pubmed-15 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5389 - Rouge2 Precision: 0.7165 - Rouge2 Recall: 0.5375 - Rouge2 Fmeasure: 0.5981 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.1024 \| 0.75 \| 500 \| 0.7890 \| 0.6854 \| 0.4813 \| 0.5502 \| \| 0.8788 \| 1.51 \| 1000 \| 0.7176 \| 0.6906 \| 0.4989 \| 0.5638 \| \| 0.8086 \| 2.26 \| 1500 \| 0.6830 \| 0.6872 \| 0.5052 \| 0.5663 \| \| 0.7818 \| 3.02 \| 2000 \| 0.6650 \| 0.6912 \| 0.5104 \| 0.5711 \| \| 0.7466 \| 3.77 \| 2500 \| 0.6458 \| 0.6965 \| 0.5167 \| 0.5774 \| \| 0.731 \| 4.52 \| 3000 \| 0.6355 \| 0.6955 \| 0.5161 \| 0.5763 \| \| 0.7126 \| 5.28 \| 3500 \| 0.6249 \| 0.6924 \| 0.517 \| 0.576 \| \| 0.6998 \| 6.03 \| 4000 \| 0.6166 \| 0.6995 \| 0.5207 \| 0.5809 \| \| 0.6855 \| 6.79 \| 4500 \| 0.6076 \| 0.6981 \| 0.5215 \| 0.5813 \| \| 0.676 \| 7.54 \| 5000 \| 0.6015 \| 0.7003 \| 0.5242 \| 0.5836 \| \| 0.6688 \| 8.3 \| 5500 \| 0.5962 \| 0.7004 \| 0.5235 \| 0.583 \| \| 0.6569 \| 9.05 \| 6000 \| 0.5900 \| 0.6997 \| 0.5234 \| 0.5827 \| \| 0.6503 \| 9.8 \| 6500 \| 0.5880 \| 0.703 \| 0.5257 \| 0.5856 \| \| 0.6455 \| 10.56 \| 7000 \| 0.5818 \| 0.7008 \| 0.5259 \| 0.5849 \| \| 0.635 \| 11.31 \| 7500 \| 0.5796 \| 0.7017 \| 0.5271 \| 0.5861 \| \| 0.6323 \| 12.07 \| 8000 \| 0.5769 \| 0.7053 \| 0.5276 \| 0.5877 \| \| 0.6241 \| 12.82 \| 8500 \| 0.5730 \| 0.7011 \| 0.5243 \| 0.5838 \| \| 0.6224 \| 13.57 \| 9000 \| 0.5696 \| 0.7046 \| 0.5286 \| 0.5879 \| \| 0.6139 \| 14.33 \| 9500 \| 0.5685 \| 0.7047 \| 0.5295 \| 0.5886 \| \| 0.6118 \| 15.08 \| 10000 \| 0.5653 \| 0.704 \| 0.5297 \| 0.5886 \| \| 0.6089 \| 15.84 \| 10500 \| 0.5633 \| 0.703 \| 0.5272 \| 0.5865 \| \| 0.598 \| 16.59 \| 11000 \| 0.5613 \| 0.7059 \| 0.5293 \| 0.5889 \| \| 0.6003 \| 17.35 \| 11500 \| 0.5602 \| 0.7085 \| 0.532 \| 0.5918 \| \| 0.5981 \| 18.1 \| 12000 \| 0.5587 \| 0.7106 \| 0.5339 \| 0.5938 \| \| 0.5919 \| 18.85 \| 12500 \| 0.5556 \| 0.708 \| 0.5319 \| 0.5914 \| \| 0.5897 \| 19.61 \| 13000 \| 0.5556 \| 0.7106 \| 0.5327 \| 0.5931 \| \| 0.5899 \| 20.36 \| 13500 \| 0.5526 \| 0.7114 \| 0.534 \| 0.5939 \| \| 0.5804 \| 21.12 \| 14000 \| 0.5521 \| 0.7105 \| 0.5328 \| 0.5928 \| \| 0.5764 \| 21.87 \| 14500 \| 0.5520 \| 0.715 \| 0.537 \| 0.5976 \| \| 0.5793 \| 22.62 \| 15000 \| 0.5506 \| 0.713 \| 0.5346 \| 0.5951 \| \| 0.5796 \| 23.38 \| 15500 \| 0.5492 \| 0.7124 \| 0.5352 \| 0.5952 \| \| 0.5672 \| 24.13 \| 16000 \| 0.5482 \| 0.7124 \| 0.5346 \| 0.5948 \| \| 0.5737 \| 24.89 \| 16500 \| 0.5470 \| 0.7134 \| 0.5352 \| 0.5956 \| \| 0.5685 \| 25.64 \| 17000 \| 0.5463 \| 0.7117 \| 0.5346 \| 0.5946 \| \| 0.5658 \| 26.4 \| 17500 \| 0.5457 \| 0.7145 \| 0.5359 \| 0.5965 \| \| 0.5657 \| 27.15 \| 18000 \| 0.5447 \| 0.7145 \| 0.5367 \| 0.597 \| \| 0.5645 \| 27.9 \| 18500 \| 0.5441 \| 0.7141 \| 0.5362 \| 0.5964 \| \| 0.565 \| 28.66 \| 19000 \| 0.5436 \| 0.7151 \| 0.5367 \| 0.5972 \| \| 0.5579 \| 29.41 \| 19500 \| 0.5426 \| 0.7162 \| 0.5378 \| 0.5982 \| \| 0.563 \| 30.17 \| 20000 \| 0.5424 \| 0.7155 \| 0.5373 \| 0.5977 \| \| 0.556 \| 30.92 \| 20500 \| 0.5418 \| 0.7148 \| 0.536 \| 0.5966 \| \| 0.5576 \| 31.67 \| 21000 \| 0.5411 \| 0.7141 \| 0.5356 \| 0.5961 \| \| 0.5546 \| 32.43 \| 21500 \| 0.5409 \| 0.7149 \| 0.5364 \| 0.5967 \| \| 0.556 \| 33.18 \| 22000 \| 0.5405 \| 0.7143 \| 0.5356 \| 0.596 \| \| 0.5536 \| 33.94 \| 22500 \| 0.5401 \| 0.7165 \| 0.5377 \| 0.5982 \| \| 0.5527 \| 34.69 \| 23000 \| 0.5397 \| 0.7188 \| 0.5389 \| 0.5999 \| \| 0.5531 \| 35.44 \| 23500 \| 0.5395 \| 0.7172 \| 0.538 \| 0.5989 \| \| 0.5508 \| 36.2 \| 24000 \| 0.5392 \| 0.7166 \| 0.538 \| 0.5985 \| \| 0.5495 \| 36.95 \| 24500 \| 0.5391 \| 0.7176 \| 0.5387 \| 0.5993 \| \| 0.5539 \| 37.71 \| 25000 \| 0.5391 \| 0.7169 \| 0.5372 \| 0.598 \| \| 0.5452 \| 38.46 \| 25500 \| 0.5390 \| 0.7179 \| 0.5384 \| 0.5991 \| \| 0.5513 \| 39.22 \| 26000 \| 0.5390 \| 0.717 \| 0.5377 \| 0.5984 \| \| 0.5506 \| 39.97 \| 26500 \| 0.5389 \| 0.7165 \| 0.5375 \| 0.5981 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-xlarge-spanish-finetuned-pos	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed-35 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-mlm-pubmed-35 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1101 - Rouge2 Precision: 0.4758 - Rouge2 Recall: 0.3498 - Rouge2 Fmeasure: 0.3927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.8404 \| 0.75 \| 500 \| 1.5005 \| 0.4265 \| 0.2786 \| 0.3273 \| \| 1.6858 \| 1.51 \| 1000 \| 1.4216 \| 0.4318 \| 0.2946 \| 0.3404 \| \| 1.6071 \| 2.26 \| 1500 \| 1.3777 \| 0.4472 \| 0.3148 \| 0.3598 \| \| 1.5551 \| 3.02 \| 2000 \| 1.3360 \| 0.4406 \| 0.3168 \| 0.3586 \| \| 1.5116 \| 3.77 \| 2500 \| 1.3128 \| 0.4523 \| 0.3234 \| 0.3671 \| \| 1.4837 \| 4.52 \| 3000 \| 1.2937 \| 0.4477 \| 0.3215 \| 0.3645 \| \| 1.4513 \| 5.28 \| 3500 \| 1.2766 \| 0.4511 \| 0.3262 \| 0.3689 \| \| 1.4336 \| 6.03 \| 4000 \| 1.2626 \| 0.4548 \| 0.3283 \| 0.3718 \| \| 1.4149 \| 6.79 \| 4500 \| 1.2449 \| 0.4495 \| 0.3274 \| 0.3687 \| \| 1.3977 \| 7.54 \| 5000 \| 1.2349 \| 0.4507 \| 0.3305 \| 0.3712 \| \| 1.3763 \| 8.3 \| 5500 \| 1.2239 \| 0.4519 \| 0.3266 \| 0.3688 \| \| 1.371 \| 9.05 \| 6000 \| 1.2171 \| 0.4546 \| 0.3305 \| 0.3727 \| \| 1.3501 \| 9.8 \| 6500 \| 1.2080 \| 0.4575 \| 0.3329 \| 0.3755 \| \| 1.3443 \| 10.56 \| 7000 \| 1.2017 \| 0.4576 \| 0.3314 \| 0.3742 \| \| 1.326 \| 11.31 \| 7500 \| 1.1926 \| 0.4578 \| 0.333 \| 0.3757 \| \| 1.3231 \| 12.07 \| 8000 \| 1.1866 \| 0.4606 \| 0.3357 \| 0.3782 \| \| 1.3089 \| 12.82 \| 8500 \| 1.1816 \| 0.4591 \| 0.3338 \| 0.3765 \| \| 1.3007 \| 13.57 \| 9000 \| 1.1764 \| 0.4589 \| 0.3361 \| 0.3777 \| \| 1.2943 \| 14.33 \| 9500 \| 1.1717 \| 0.4641 \| 0.3382 \| 0.3811 \| \| 1.2854 \| 15.08 \| 10000 \| 1.1655 \| 0.4617 \| 0.3378 \| 0.38 \| \| 1.2777 \| 15.84 \| 10500 \| 1.1612 \| 0.464 \| 0.3401 \| 0.3823 \| \| 1.2684 \| 16.59 \| 11000 \| 1.1581 \| 0.4608 \| 0.3367 \| 0.3789 \| \| 1.2612 \| 17.35 \| 11500 \| 1.1554 \| 0.4623 \| 0.3402 \| 0.3818 \| \| 1.2625 \| 18.1 \| 12000 \| 1.1497 \| 0.4613 \| 0.3381 \| 0.3802 \| \| 1.2529 \| 18.85 \| 12500 \| 1.1465 \| 0.4671 \| 0.3419 \| 0.3848 \| \| 1.2461 \| 19.61 \| 13000 \| 1.1431 \| 0.4646 \| 0.3399 \| 0.3824 \| \| 1.2415 \| 20.36 \| 13500 \| 1.1419 \| 0.4659 \| 0.341 \| 0.3835 \| \| 1.2375 \| 21.12 \| 14000 \| 1.1377 \| 0.4693 \| 0.3447 \| 0.3873 \| \| 1.2315 \| 21.87 \| 14500 \| 1.1353 \| 0.4672 \| 0.3433 \| 0.3855 \| \| 1.2263 \| 22.62 \| 15000 \| 1.1333 \| 0.467 \| 0.3433 \| 0.3854 \| \| 1.2214 \| 23.38 \| 15500 \| 1.1305 \| 0.4682 \| 0.3446 \| 0.3869 \| \| 1.2202 \| 24.13 \| 16000 \| 1.1291 \| 0.4703 \| 0.3465 \| 0.3888 \| \| 1.2155 \| 24.89 \| 16500 \| 1.1270 \| 0.472 \| 0.348 \| 0.3903 \| \| 1.2064 \| 25.64 \| 17000 \| 1.1261 \| 0.4724 \| 0.3479 \| 0.3905 \| \| 1.2173 \| 26.4 \| 17500 \| 1.1236 \| 0.4734 \| 0.3485 \| 0.3912 \| \| 1.1994 \| 27.15 \| 18000 \| 1.1220 \| 0.4739 \| 0.3486 \| 0.3915 \| \| 1.2018 \| 27.9 \| 18500 \| 1.1217 \| 0.4747 \| 0.3489 \| 0.3921 \| \| 1.2045 \| 28.66 \| 19000 \| 1.1194 \| 0.4735 \| 0.3488 \| 0.3916 \| \| 1.1949 \| 29.41 \| 19500 \| 1.1182 \| 0.4732 \| 0.3484 \| 0.3911 \| \| 1.19 \| 30.17 \| 20000 \| 1.1166 \| 0.4724 \| 0.3479 \| 0.3904 \| \| 1.1932 \| 30.92 \| 20500 \| 1.1164 \| 0.4753 \| 0.3494 \| 0.3924 \| \| 1.1952 \| 31.67 \| 21000 \| 1.1147 \| 0.4733 \| 0.3485 \| 0.3911 \| \| 1.1922 \| 32.43 \| 21500 \| 1.1146 \| 0.475 \| 0.3494 \| 0.3923 \| \| 1.1889 \| 33.18 \| 22000 \| 1.1132 \| 0.4765 \| 0.3499 \| 0.3933 \| \| 1.1836 \| 33.94 \| 22500 \| 1.1131 \| 0.4768 \| 0.351 \| 0.3939 \| \| 1.191 \| 34.69 \| 23000 \| 1.1127 \| 0.4755 \| 0.3495 \| 0.3926 \| \| 1.1811 \| 35.44 \| 23500 \| 1.1113 \| 0.4748 \| 0.349 \| 0.3919 \| \| 1.1864 \| 36.2 \| 24000 \| 1.1107 \| 0.4751 \| 0.3494 \| 0.3921 \| \| 1.1789 \| 36.95 \| 24500 \| 1.1103 \| 0.4756 \| 0.3499 \| 0.3927 \| \| 1.1819 \| 37.71 \| 25000 \| 1.1101 \| 0.4758 \| 0.35 \| 0.3932 \| \| 1.1862 \| 38.46 \| 25500 \| 1.1099 \| 0.4755 \| 0.3497 \| 0.3926 \| \| 1.1764 \| 39.22 \| 26000 \| 1.1101 \| 0.4759 \| 0.3498 \| 0.3928 \| \| 1.1819 \| 39.97 \| 26500 \| 1.1101 \| 0.4758 \| 0.3498 \| 0.3927 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa	[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed-45 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-mlm-pubmed-45 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6395 - Rouge2 Precision: 0.3383 - Rouge2 Recall: 0.2424 - Rouge2 Fmeasure: 0.2753 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 2.519 \| 0.75 \| 500 \| 1.9659 \| 0.3178 \| 0.1888 \| 0.2299 \| \| 2.169 \| 1.51 \| 1000 \| 1.8450 \| 0.3256 \| 0.2138 \| 0.25 \| \| 2.0796 \| 2.26 \| 1500 \| 1.7900 \| 0.3368 \| 0.2265 \| 0.2636 \| \| 1.9978 \| 3.02 \| 2000 \| 1.7553 \| 0.3427 \| 0.234 \| 0.2709 \| \| 1.9686 \| 3.77 \| 2500 \| 1.7172 \| 0.3356 \| 0.2347 \| 0.2692 \| \| 1.9142 \| 4.52 \| 3000 \| 1.6986 \| 0.3358 \| 0.238 \| 0.2715 \| \| 1.921 \| 5.28 \| 3500 \| 1.6770 \| 0.3349 \| 0.2379 \| 0.2709 \| \| 1.8848 \| 6.03 \| 4000 \| 1.6683 \| 0.3346 \| 0.2379 \| 0.2708 \| \| 1.8674 \| 6.79 \| 4500 \| 1.6606 \| 0.3388 \| 0.2419 \| 0.2752 \| \| 1.8606 \| 7.54 \| 5000 \| 1.6514 \| 0.3379 \| 0.2409 \| 0.274 \| \| 1.8515 \| 8.3 \| 5500 \| 1.6438 \| 0.3356 \| 0.2407 \| 0.2731 \| \| 1.8403 \| 9.05 \| 6000 \| 1.6401 \| 0.3367 \| 0.2421 \| 0.2744 \| \| 1.8411 \| 9.8 \| 6500 \| 1.6395 \| 0.3383 \| 0.2424 \| 0.2753 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-xlarge-spanish-finetuned-xnli	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	29	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-mlm-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8008 - Rouge2 Precision: 0.6071 - Rouge2 Recall: 0.4566 - Rouge2 Fmeasure: 0.5079 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.914 \| 0.75 \| 500 \| 0.8691 \| 0.5901 \| 0.4357 \| 0.4879 \| \| 0.9093 \| 1.51 \| 1000 \| 0.8646 \| 0.5867 \| 0.4372 \| 0.488 \| \| 0.895 \| 2.26 \| 1500 \| 0.8618 \| 0.5891 \| 0.4387 \| 0.49 \| \| 0.8842 \| 3.02 \| 2000 \| 0.8571 \| 0.5899 \| 0.4374 \| 0.4891 \| \| 0.8796 \| 3.77 \| 2500 \| 0.8544 \| 0.5903 \| 0.4406 \| 0.4916 \| \| 0.8759 \| 4.52 \| 3000 \| 0.8513 \| 0.5921 \| 0.4395 \| 0.4912 \| \| 0.8621 \| 5.28 \| 3500 \| 0.8485 \| 0.5934 \| 0.4413 \| 0.493 \| \| 0.8613 \| 6.03 \| 4000 \| 0.8442 \| 0.5944 \| 0.4428 \| 0.4944 \| \| 0.8537 \| 6.79 \| 4500 \| 0.8406 \| 0.594 \| 0.4414 \| 0.4932 \| \| 0.8518 \| 7.54 \| 5000 \| 0.8399 \| 0.5956 \| 0.4424 \| 0.4945 \| \| 0.8438 \| 8.3 \| 5500 \| 0.8365 \| 0.5953 \| 0.4452 \| 0.4964 \| \| 0.8339 \| 9.05 \| 6000 \| 0.8353 \| 0.5983 \| 0.4468 \| 0.4983 \| \| 0.8307 \| 9.8 \| 6500 \| 0.8331 \| 0.5979 \| 0.4461 \| 0.4976 \| \| 0.8328 \| 10.56 \| 7000 \| 0.8304 \| 0.5975 \| 0.4465 \| 0.4979 \| \| 0.8263 \| 11.31 \| 7500 \| 0.8283 \| 0.5977 \| 0.4467 \| 0.4981 \| \| 0.8168 \| 12.07 \| 8000 \| 0.8267 \| 0.5971 \| 0.4463 \| 0.4976 \| \| 0.8165 \| 12.82 \| 8500 \| 0.8248 \| 0.5969 \| 0.4462 \| 0.4976 \| \| 0.8084 \| 13.57 \| 9000 \| 0.8245 \| 0.6018 \| 0.4527 \| 0.5035 \| \| 0.8136 \| 14.33 \| 9500 \| 0.8219 \| 0.6023 \| 0.4509 \| 0.5023 \| \| 0.8073 \| 15.08 \| 10000 \| 0.8206 \| 0.6002 \| 0.4486 \| 0.5001 \| \| 0.808 \| 15.84 \| 10500 \| 0.8185 \| 0.6009 \| 0.4506 \| 0.5019 \| \| 0.8027 \| 16.59 \| 11000 \| 0.8173 \| 0.5978 \| 0.4478 \| 0.4989 \| \| 0.8061 \| 17.35 \| 11500 \| 0.8169 \| 0.6022 \| 0.4513 \| 0.5026 \| \| 0.7922 \| 18.1 \| 12000 \| 0.8152 \| 0.6016 \| 0.4501 \| 0.5016 \| \| 0.7928 \| 18.85 \| 12500 \| 0.8141 \| 0.6009 \| 0.45 \| 0.5012 \| \| 0.7909 \| 19.61 \| 13000 \| 0.8143 \| 0.6019 \| 0.4521 \| 0.5028 \| \| 0.7909 \| 20.36 \| 13500 \| 0.8115 \| 0.5997 \| 0.4505 \| 0.5011 \| \| 0.7949 \| 21.12 \| 14000 \| 0.8115 \| 0.6043 \| 0.4536 \| 0.5048 \| \| 0.7853 \| 21.87 \| 14500 \| 0.8095 \| 0.6033 \| 0.4527 \| 0.5038 \| \| 0.7819 \| 22.62 \| 15000 \| 0.8095 \| 0.6054 \| 0.4541 \| 0.5056 \| \| 0.7828 \| 23.38 \| 15500 \| 0.8075 \| 0.6036 \| 0.453 \| 0.5042 \| \| 0.787 \| 24.13 \| 16000 \| 0.8068 \| 0.6031 \| 0.4528 \| 0.504 \| \| 0.7739 \| 24.89 \| 16500 \| 0.8072 \| 0.6043 \| 0.4529 \| 0.5045 \| \| 0.7782 \| 25.64 \| 17000 \| 0.8073 \| 0.606 \| 0.4551 \| 0.5063 \| \| 0.7772 \| 26.4 \| 17500 \| 0.8063 \| 0.6055 \| 0.4549 \| 0.5062 \| \| 0.7718 \| 27.15 \| 18000 \| 0.8057 \| 0.606 \| 0.4546 \| 0.5059 \| \| 0.7747 \| 27.9 \| 18500 \| 0.8045 \| 0.6046 \| 0.4543 \| 0.5054 \| \| 0.7738 \| 28.66 \| 19000 \| 0.8035 \| 0.6059 \| 0.4549 \| 0.506 \| \| 0.7642 \| 29.41 \| 19500 \| 0.8041 \| 0.6053 \| 0.4545 \| 0.5058 \| \| 0.7666 \| 30.17 \| 20000 \| 0.8039 \| 0.6066 \| 0.457 \| 0.508 \| \| 0.7686 \| 30.92 \| 20500 \| 0.8027 \| 0.6075 \| 0.4571 \| 0.5081 \| \| 0.7664 \| 31.67 \| 21000 \| 0.8026 \| 0.6062 \| 0.4566 \| 0.5076 \| \| 0.77 \| 32.43 \| 21500 \| 0.8022 \| 0.6068 \| 0.4571 \| 0.5081 \| \| 0.7618 \| 33.18 \| 22000 \| 0.8015 \| 0.6065 \| 0.4563 \| 0.5072 \| \| 0.7615 \| 33.94 \| 22500 \| 0.8013 \| 0.6064 \| 0.4565 \| 0.5074 \| \| 0.7611 \| 34.69 \| 23000 \| 0.8017 \| 0.607 \| 0.4567 \| 0.5078 \| \| 0.7611 \| 35.44 \| 23500 \| 0.8013 \| 0.608 \| 0.4565 \| 0.5082 \| \| 0.7604 \| 36.2 \| 24000 \| 0.8012 \| 0.6069 \| 0.4561 \| 0.5072 \| \| 0.7599 \| 36.95 \| 24500 \| 0.8013 \| 0.6078 \| 0.4571 \| 0.5085 \| \| 0.7542 \| 37.71 \| 25000 \| 0.8016 \| 0.6083 \| 0.4579 \| 0.5091 \| \| 0.7637 \| 38.46 \| 25500 \| 0.8009 \| 0.6072 \| 0.4569 \| 0.5081 \| \| 0.7596 \| 39.22 \| 26000 \| 0.8008 \| 0.6069 \| 0.4566 \| 0.5078 \| \| 0.7604 \| 39.97 \| 26500 \| 0.8008 \| 0.6071 \| 0.4566 \| 0.5079 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-xxlarge-spanish-finetuned-mldoc	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	26	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-paraphrase-pubmed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-paraphrase-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4032 - Rouge2 Precision: 0.8281 - Rouge2 Recall: 0.6346 - Rouge2 Fmeasure: 0.6996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 0.5253 \| 1.0 \| 663 \| 0.4895 \| 0.8217 \| 0.6309 \| 0.695 \| \| 0.5385 \| 2.0 \| 1326 \| 0.4719 \| 0.822 \| 0.6307 \| 0.6953 \| \| 0.5255 \| 3.0 \| 1989 \| 0.4579 \| 0.8225 \| 0.631 \| 0.6954 \| \| 0.4927 \| 4.0 \| 2652 \| 0.4510 \| 0.824 \| 0.6315 \| 0.6965 \| \| 0.484 \| 5.0 \| 3315 \| 0.4426 \| 0.8254 \| 0.6323 \| 0.6974 \| \| 0.4691 \| 6.0 \| 3978 \| 0.4383 \| 0.8241 \| 0.6311 \| 0.6962 \| \| 0.4546 \| 7.0 \| 4641 \| 0.4319 \| 0.8248 \| 0.6322 \| 0.6969 \| \| 0.4431 \| 8.0 \| 5304 \| 0.4270 \| 0.8254 \| 0.633 \| 0.6977 \| \| 0.4548 \| 9.0 \| 5967 \| 0.4257 \| 0.8257 \| 0.6322 \| 0.6976 \| \| 0.4335 \| 10.0 \| 6630 \| 0.4241 \| 0.8271 \| 0.6333 \| 0.6986 \| \| 0.4234 \| 11.0 \| 7293 \| 0.4203 \| 0.827 \| 0.6341 \| 0.6992 \| \| 0.433 \| 12.0 \| 7956 \| 0.4185 \| 0.8279 \| 0.6347 \| 0.6998 \| \| 0.4108 \| 13.0 \| 8619 \| 0.4161 \| 0.8285 \| 0.6352 \| 0.7004 \| \| 0.4101 \| 14.0 \| 9282 \| 0.4133 \| 0.8289 \| 0.6356 \| 0.7008 \| \| 0.4155 \| 15.0 \| 9945 \| 0.4149 \| 0.8279 \| 0.635 \| 0.6998 \| \| 0.3991 \| 16.0 \| 10608 \| 0.4124 \| 0.8289 \| 0.6353 \| 0.7005 \| \| 0.3962 \| 17.0 \| 11271 \| 0.4113 \| 0.829 \| 0.6353 \| 0.7006 \| \| 0.3968 \| 18.0 \| 11934 \| 0.4114 \| 0.8285 \| 0.6352 \| 0.7002 \| \| 0.3962 \| 19.0 \| 12597 \| 0.4100 \| 0.8282 \| 0.6346 \| 0.6998 \| \| 0.3771 \| 20.0 \| 13260 \| 0.4078 \| 0.829 \| 0.6352 \| 0.7005 \| \| 0.3902 \| 21.0 \| 13923 \| 0.4083 \| 0.8295 \| 0.6351 \| 0.7006 \| \| 0.3811 \| 22.0 \| 14586 \| 0.4077 \| 0.8276 \| 0.6346 \| 0.6995 \| \| 0.38 \| 23.0 \| 15249 \| 0.4076 \| 0.8281 \| 0.6346 \| 0.6997 \| \| 0.3695 \| 24.0 \| 15912 \| 0.4059 \| 0.8277 \| 0.6344 \| 0.6993 \| \| 0.3665 \| 25.0 \| 16575 \| 0.4043 \| 0.8278 \| 0.6343 \| 0.6992 \| \| 0.3728 \| 26.0 \| 17238 \| 0.4059 \| 0.8279 \| 0.6346 \| 0.6994 \| \| 0.3669 \| 27.0 \| 17901 \| 0.4048 \| 0.8271 \| 0.6342 \| 0.6991 \| \| 0.3702 \| 28.0 \| 18564 \| 0.4058 \| 0.8265 \| 0.6338 \| 0.6985 \| \| 0.3674 \| 29.0 \| 19227 \| 0.4049 \| 0.8277 \| 0.6345 \| 0.6993 \| \| 0.364 \| 30.0 \| 19890 \| 0.4048 \| 0.8273 \| 0.6341 \| 0.699 \| \| 0.3618 \| 31.0 \| 20553 \| 0.4041 \| 0.828 \| 0.6349 \| 0.6997 \| \| 0.3609 \| 32.0 \| 21216 \| 0.4040 \| 0.8275 \| 0.6346 \| 0.6994 \| \| 0.357 \| 33.0 \| 21879 \| 0.4037 \| 0.8278 \| 0.6348 \| 0.6996 \| \| 0.3638 \| 34.0 \| 22542 \| 0.4038 \| 0.8275 \| 0.634 \| 0.6989 \| \| 0.3551 \| 35.0 \| 23205 \| 0.4035 \| 0.8275 \| 0.6344 \| 0.6992 \| \| 0.358 \| 36.0 \| 23868 \| 0.4035 \| 0.8279 \| 0.6347 \| 0.6995 \| \| 0.3519 \| 37.0 \| 24531 \| 0.4034 \| 0.8277 \| 0.6343 \| 0.6992 \| \| 0.359 \| 38.0 \| 25194 \| 0.4035 \| 0.8281 \| 0.6346 \| 0.6996 \| \| 0.3542 \| 39.0 \| 25857 \| 0.4033 \| 0.8281 \| 0.6346 \| 0.6996 \| \| 0.3592 \| 40.0 \| 26520 \| 0.4032 \| 0.8281 \| 0.6346 \| 0.6996 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
dccuchile/albert-xxlarge-spanish-finetuned-ner	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	28	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9233262687967644 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2180 - Accuracy: 0.923 - F1: 0.9233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8217 \| 1.0 \| 250 \| 0.3137 \| 0.903 \| 0.8999 \| \| 0.2484 \| 2.0 \| 500 \| 0.2180 \| 0.923 \| 0.9233 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
dccuchile/albert-xxlarge-spanish-finetuned-pawsx	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	26	null	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - gborn/autonlp-data-news-summarization co2_eq_emissions: 210.6348731063569 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 483413089 - CO2 Emissions (in grams): 210.6348731063569 ## Validation Metrics - Loss: 1.8478657007217407 - Rouge1: 50.5981 - Rouge2: 26.2167 - RougeL: 46.0513 - RougeLsum: 46.061 - Gen Len: 13.5987 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/gborn/autonlp-news-summarization-483413089 ```
dccuchile/albert-xxlarge-spanish-finetuned-pos	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-cased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5956649094312695 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6747 - Matthews Correlation: 0.5957 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name cola \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-cola \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.4921 \| 1.0 \| 535 \| 0.5283 \| 0.5068 \| \| 0.2837 \| 2.0 \| 1070 \| 0.5133 \| 0.5521 \| \| 0.1775 \| 3.0 \| 1605 \| 0.6747 \| 0.5957 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa	[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-mnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.8410292921074044 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-mnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5721 - Accuracy: 0.8410 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-mnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.5323 \| 1.0 \| 24544 \| 0.4431 \| 0.8302 \| \| 0.3447 \| 2.0 \| 49088 \| 0.4725 \| 0.8353 \| \| 0.2267 \| 3.0 \| 73632 \| 0.5887 \| 0.8368 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/albert-xxlarge-spanish-finetuned-xnli	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	68	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-cased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8602941176470589 - name: F1 type: f1 value: 0.9025641025641027 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.7132 - Accuracy: 0.8603 - F1: 0.9026 - Combined Score: 0.8814 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.5981 \| 1.0 \| 230 \| 0.4580 \| 0.7892 \| 0.8562 \| 0.8227 \| \| 0.3739 \| 2.0 \| 460 \| 0.3806 \| 0.8480 \| 0.8942 \| 0.8711 \| \| 0.1991 \| 3.0 \| 690 \| 0.4879 \| 0.8529 \| 0.8958 \| 0.8744 \| \| 0.1286 \| 4.0 \| 920 \| 0.6342 \| 0.8529 \| 0.8986 \| 0.8758 \| \| 0.0812 \| 5.0 \| 1150 \| 0.7132 \| 0.8603 \| 0.9026 \| 0.8814 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/albert-base-spanish	[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]	null	{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	586	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.9099395936298736 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-qnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3986 - Accuracy: 0.9099 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:---------------:\| \| 0.337 \| 1.0 \| 6547 \| 0.9013 \| 0.2448 \| \| 0.1971 \| 2.0 \| 13094 \| 0.9143 \| 0.2839 \| \| 0.1175 \| 3.0 \| 19641 \| 0.9099 \| 0.3986 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/albert-large-spanish	[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]	null	{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	75	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-cased-finetuned-qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue args: qqp metrics: - name: Accuracy type: accuracy value: 0.9083848627256987 - name: F1 type: f1 value: 0.8767633750332712 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-qqp This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.3752 - Accuracy: 0.9084 - F1: 0.8768 - Combined Score: 0.8926 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.308 \| 1.0 \| 22741 \| 0.2548 \| 0.8925 \| 0.8556 \| 0.8740 \| \| 0.201 \| 2.0 \| 45482 \| 0.2881 \| 0.9032 \| 0.8698 \| 0.8865 \| \| 0.1416 \| 3.0 \| 68223 \| 0.3752 \| 0.9084 \| 0.8768 \| 0.8926 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/albert-tiny-spanish	[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]	null	{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	393	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6714801444043321 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-rte This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7260 - Accuracy: 0.6715 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6915 \| 1.0 \| 156 \| 0.6491 \| 0.6606 \| \| 0.55 \| 2.0 \| 312 \| 0.6737 \| 0.6570 \| \| 0.3955 \| 3.0 \| 468 \| 0.7260 \| 0.6715 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/albert-xlarge-spanish	[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]	null	{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	91	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9231651376146789 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-sst2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3649 - Accuracy: 0.9232 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name sst2 \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-sst2 \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:---------------:\| \| 0.233 \| 1.0 \| 4210 \| 0.9174 \| 0.2841 \| \| 0.1261 \| 2.0 \| 8420 \| 0.9278 \| 0.3310 \| \| 0.0768 \| 3.0 \| 12630 \| 0.9232 \| 0.3649 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/albert-xxlarge-spanish	[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]	null	{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	42	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - spearmanr model-index: - name: bert-base-cased-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8897907271421561 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-stsb This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4861 - Pearson: 0.8926 - Spearmanr: 0.8898 - Combined Score: 0.8912 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Combined Score \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:--------------:\|:---------------:\|:-------:\|:---------:\| \| 1.1174 \| 1.0 \| 360 \| 0.8816 \| 0.5000 \| 0.8832 \| 0.8800 \| \| 0.3835 \| 2.0 \| 720 \| 0.8901 \| 0.4672 \| 0.8915 \| 0.8888 \| \| 0.2388 \| 3.0 \| 1080 \| 0.8912 \| 0.4861 \| 0.8926 \| 0.8898 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	27	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.4647887323943662 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-wnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6996 - Accuracy: 0.4648 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name wnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-wnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7299 \| 1.0 \| 40 \| 0.6923 \| 0.5634 \| \| 0.6982 \| 2.0 \| 80 \| 0.7027 \| 0.3803 \| \| 0.6972 \| 3.0 \| 120 \| 0.7005 \| 0.4507 \| \| 0.6992 \| 4.0 \| 160 \| 0.6977 \| 0.5352 \| \| 0.699 \| 5.0 \| 200 \| 0.6996 \| 0.4648 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-cased-finetuned-ner	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	81	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-large-cased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5957317644481708 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-finetuned-cola This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.8385 - Matthews Correlation: 0.5957 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5533 \| 1.0 \| 2138 \| 0.7943 \| 0.4439 \| \| 0.5004 \| 2.0 \| 4276 \| 0.7272 \| 0.5678 \| \| 0.2865 \| 3.0 \| 6414 \| 0.8385 \| 0.5957 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	25	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-large-cased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.6838235294117647 - name: F1 type: f1 value: 0.8122270742358079 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-finetuned-mrpc This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6274 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.6441 \| 1.0 \| 917 \| 0.6370 \| 0.6838 \| 0.8122 \| 0.7480 \| \| 0.6451 \| 2.0 \| 1834 \| 0.6553 \| 0.6838 \| 0.8122 \| 0.7480 \| \| 0.6428 \| 3.0 \| 2751 \| 0.6332 \| 0.6838 \| 0.8122 \| 0.7480 \| \| 0.6476 \| 4.0 \| 3668 \| 0.6248 \| 0.6838 \| 0.8122 \| 0.7480 \| \| 0.6499 \| 5.0 \| 4585 \| 0.6274 \| 0.6838 \| 0.8122 \| 0.7480 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-cased-finetuned-pos	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-large-cased-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6642599277978339 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-finetuned-rte This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 1.5187 - Accuracy: 0.6643 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6969 \| 1.0 \| 623 \| 0.7039 \| 0.5343 \| \| 0.5903 \| 2.0 \| 1246 \| 0.6461 \| 0.7184 \| \| 0.4557 \| 3.0 \| 1869 \| 1.5187 \| 0.6643 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa	[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	"2021-09-23T04:24:07Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-large-cased-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.352112676056338 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-finetuned-wnli This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.7087 - Accuracy: 0.3521 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:--------:\|:---------------:\| \| 0.7114 \| 1.0 \| 159 \| 0.5634 \| 0.6923 \| \| 0.7141 \| 2.0 \| 318 \| 0.5634 \| 0.6895 \| \| 0.7063 \| 3.0 \| 477 \| 0.5634 \| 0.6930 \| \| 0.712 \| 4.0 \| 636 \| 0.4507 \| 0.7077 \| \| 0.7037 \| 5.0 \| 795 \| 0.3521 \| 0.7087 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	28	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-base-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.35940659235571387 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-cola This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.5929 - Matthews Correlation: 0.3594 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name cola \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-cola \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5895 \| 1.0 \| 535 \| 0.6146 \| 0.1699 \| \| 0.4656 \| 2.0 \| 1070 \| 0.5667 \| 0.3047 \| \| 0.3329 \| 3.0 \| 1605 \| 0.5929 \| 0.3594 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	39	"2021-09-17T07:11:04Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-mnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.7674938974776241 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-mnli This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6443 - Accuracy: 0.7675 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-mnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.7143 \| 1.0 \| 24544 \| 0.6169 \| 0.7504 \| \| 0.5407 \| 2.0 \| 49088 \| 0.6218 \| 0.7627 \| \| 0.4178 \| 3.0 \| 73632 \| 0.6564 \| 0.7658 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	"2021-09-16T17:30:22Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-base-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.7720588235294118 - name: F1 type: f1 value: 0.8502415458937198 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-mrpc This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.9653 - Accuracy: 0.7721 - F1: 0.8502 - Combined Score: 0.8112 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.544 \| 1.0 \| 230 \| 0.5272 \| 0.7328 \| 0.8300 \| 0.7814 \| \| 0.4034 \| 2.0 \| 460 \| 0.6211 \| 0.7255 \| 0.8298 \| 0.7776 \| \| 0.2602 \| 3.0 \| 690 \| 0.9110 \| 0.7230 \| 0.8306 \| 0.7768 \| \| 0.1688 \| 4.0 \| 920 \| 0.8640 \| 0.7696 \| 0.8489 \| 0.8092 \| \| 0.0913 \| 5.0 \| 1150 \| 0.9653 \| 0.7721 \| 0.8502 \| 0.8112 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	24	"2021-09-17T18:09:22Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.8438586857038257 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-qnli This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4746 - Accuracy: 0.8439 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4597 \| 1.0 \| 6547 \| 0.3713 \| 0.8411 \| \| 0.3252 \| 2.0 \| 13094 \| 0.3781 \| 0.8420 \| \| 0.2243 \| 3.0 \| 19641 \| 0.4746 \| 0.8439 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	"2021-09-18T18:23:31Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-base-finetuned-qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue args: qqp metrics: - name: Accuracy type: accuracy value: 0.8847390551570616 - name: F1 type: f1 value: 0.8466197090382463 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-qqp This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.3686 - Accuracy: 0.8847 - F1: 0.8466 - Combined Score: 0.8657 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.3484 \| 1.0 \| 22741 \| 0.3014 \| 0.8676 \| 0.8297 \| 0.8487 \| \| 0.2387 \| 2.0 \| 45482 \| 0.3011 \| 0.8801 \| 0.8429 \| 0.8615 \| \| 0.1739 \| 3.0 \| 68223 \| 0.3686 \| 0.8847 \| 0.8466 \| 0.8657 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-qa-mlqa	[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	"2021-09-19T05:47:14Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.628158844765343 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-rte This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6978 - Accuracy: 0.6282 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6829 \| 1.0 \| 156 \| 0.6657 \| 0.5704 \| \| 0.6174 \| 2.0 \| 312 \| 0.6784 \| 0.6101 \| \| 0.5141 \| 3.0 \| 468 \| 0.6978 \| 0.6282 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-xnli	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	36	"2021-09-19T08:32:11Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8944954128440367 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-sst2 This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4674 - Accuracy: 0.8945 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name sst2 \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-sst2 \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:---------------:\| \| 0.2956 \| 1.0 \| 4210 \| 0.8819 \| 0.3128 \| \| 0.1746 \| 2.0 \| 8420 \| 0.8979 \| 0.3850 \| \| 0.1204 \| 3.0 \| 12630 \| 0.8945 \| 0.4674 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc	[ "pytorch", "distilbert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "DistilBertForSequenceClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	27	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - spearmanr model-index: - name: fnet-base-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8219397497728022 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-stsb This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.7894 - Pearson: 0.8256 - Spearmanr: 0.8219 - Combined Score: 0.8238 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Combined Score \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:--------------:\|:---------------:\|:-------:\|:---------:\| \| 1.5473 \| 1.0 \| 360 \| 0.8120 \| 0.7751 \| 0.8115 \| 0.8125 \| \| 0.6954 \| 2.0 \| 720 \| 0.8145 \| 0.8717 \| 0.8160 \| 0.8130 \| \| 0.4828 \| 3.0 \| 1080 \| 0.8238 \| 0.7894 \| 0.8256 \| 0.8219 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/distilbert-base-spanish-uncased-finetuned-ner	[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "DistilBertForTokenClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	28	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.5492957746478874 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-base-finetuned-wnli This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6887 - Accuracy: 0.5493 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name wnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-wnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7052 \| 1.0 \| 40 \| 0.6902 \| 0.5634 \| \| 0.6957 \| 2.0 \| 80 \| 0.7013 \| 0.4366 \| \| 0.6898 \| 3.0 \| 120 \| 0.6898 \| 0.5352 \| \| 0.6958 \| 4.0 \| 160 \| 0.6874 \| 0.5634 \| \| 0.6982 \| 5.0 \| 200 \| 0.6887 \| 0.5493 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx	[ "pytorch", "distilbert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "DistilBertForSequenceClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	29	"2021-10-09T18:55:55Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-cola-copy This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6243 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.6195 \| 1.0 \| 2138 \| 0.6527 \| 0.0 \| \| 0.6168 \| 2.0 \| 4276 \| 0.6259 \| 0.0 \| \| 0.616 \| 3.0 \| 6414 \| 0.6243 \| 0.0 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/distilbert-base-spanish-uncased-finetuned-pos	[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "DistilBertForTokenClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	"2021-10-10T05:51:58Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-cola-copy2 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6173 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.6192 \| 1.0 \| 2138 \| 0.6443 \| 0.0 \| \| 0.6177 \| 2.0 \| 4276 \| 0.6296 \| 0.0 \| \| 0.6128 \| 3.0 \| 6414 \| 0.6173 \| 0.0 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa	[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "DistilBertForQuestionAnswering" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy3 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-cola-copy3 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6554 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.6408 \| 1.0 \| 2138 \| 0.7329 \| 0.0 \| \| 0.6589 \| 2.0 \| 4276 \| 0.6311 \| 0.0 \| \| 0.6467 \| 3.0 \| 6414 \| 0.6554 \| 0.0 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
dccuchile/distilbert-base-spanish-uncased-finetuned-xnli	[ "pytorch", "distilbert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "DistilBertForSequenceClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	31	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy4 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-cola-copy4 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6500 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.6345 \| 1.0 \| 2138 \| 0.6611 \| 0.0 \| \| 0.6359 \| 2.0 \| 4276 \| 0.6840 \| 0.0 \| \| 0.6331 \| 3.0 \| 6414 \| 0.6500 \| 0.0 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
CennetOguz/distilbert-base-uncased-finetuned-recipe-1	[ "pytorch", "tensorboard", "distilbert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	"2021-09-23T07:49:09Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-cola This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6243 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.6195 \| 1.0 \| 2138 \| 0.6527 \| 0.0 \| \| 0.6168 \| 2.0 \| 4276 \| 0.6259 \| 0.0 \| \| 0.616 \| 3.0 \| 6414 \| 0.6243 \| 0.0 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate	[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-large-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8259803921568627 - name: F1 type: f1 value: 0.8798646362098139 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-mrpc This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 1.0872 - Accuracy: 0.8260 - F1: 0.8799 - Combined Score: 0.8529 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.5656 \| 1.0 \| 917 \| 0.6999 \| 0.7843 \| 0.8581 \| 0.8212 \| \| 0.3874 \| 2.0 \| 1834 \| 0.7280 \| 0.8088 \| 0.8691 \| 0.8390 \| \| 0.1627 \| 3.0 \| 2751 \| 1.1274 \| 0.8162 \| 0.8780 \| 0.8471 \| \| 0.0751 \| 4.0 \| 3668 \| 1.0289 \| 0.8333 \| 0.8870 \| 0.8602 \| \| 0.0339 \| 5.0 \| 4585 \| 1.0872 \| 0.8260 \| 0.8799 \| 0.8529 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
Certified-Zoomer/DialoGPT-small-rick	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	"2021-10-09T08:47:27Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-large-finetuned-qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue args: qqp metrics: - name: Accuracy type: accuracy value: 0.8943111550828593 - name: F1 type: f1 value: 0.8556565212985171 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-qqp This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5515 - Accuracy: 0.8943 - F1: 0.8557 - Combined Score: 0.8750 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Combined Score \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\|:------:\|:--------------:\| \| 0.4574 \| 1.0 \| 90962 \| 0.4946 \| 0.8694 \| 0.8297 \| 0.8496 \| \| 0.3387 \| 2.0 \| 181924 \| 0.4745 \| 0.8874 \| 0.8437 \| 0.8655 \| \| 0.2029 \| 3.0 \| 272886 \| 0.5515 \| 0.8943 \| 0.8557 \| 0.8750 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
Chaddmckay/Cdm	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: fnet-large-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6425992779783394 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-rte This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7528 - Accuracy: 0.6426 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7105 \| 1.0 \| 623 \| 0.6887 \| 0.5740 \| \| 0.6714 \| 2.0 \| 1246 \| 0.6742 \| 0.6209 \| \| 0.509 \| 3.0 \| 1869 \| 0.7528 \| 0.6426 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
Chae/botman	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: fnet-large-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9048165137614679 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-sst2 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5240 - Accuracy: 0.9048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.394 \| 1.0 \| 16838 \| 0.3896 \| 0.8968 \| \| 0.2076 \| 2.0 \| 33676 \| 0.5100 \| 0.8956 \| \| 0.1148 \| 3.0 \| 50514 \| 0.5240 \| 0.9048 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
Chaewon/mmnt_decoder_en	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	12	"2021-10-07T16:55:55Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: fnet-large-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8532669137129205 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-stsb This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.6250 - Pearson: 0.8554 - Spearmanr: 0.8533 - Combined Score: 0.8543 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\|:--------------:\| \| 1.0727 \| 1.0 \| 1438 \| 0.7718 \| 0.8187 \| 0.8240 \| 0.8214 \| \| 0.4619 \| 2.0 \| 2876 \| 0.7704 \| 0.8472 \| 0.8500 \| 0.8486 \| \| 0.2401 \| 3.0 \| 4314 \| 0.6250 \| 0.8554 \| 0.8533 \| 0.8543 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
Chaewon/mnmt_decoder_en	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	"2021-09-23T05:28:41Z"	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: fnet-large-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.38028169014084506 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # fnet-large-finetuned-wnli This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6953 - Accuracy: 0.3803 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7217 \| 1.0 \| 159 \| 0.6864 \| 0.5634 \| \| 0.7056 \| 2.0 \| 318 \| 0.6869 \| 0.5634 \| \| 0.706 \| 3.0 \| 477 \| 0.6875 \| 0.5634 \| \| 0.7032 \| 4.0 \| 636 \| 0.6931 \| 0.5634 \| \| 0.7025 \| 5.0 \| 795 \| 0.6953 \| 0.3803 \| ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3
chainyo/speaker-recognition-meetup	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	"2021-03-26T16:44:09Z"	--- language: cnh datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Hakha Chin by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice cnh type: common_voice args: cnh metrics: - name: Test WER type: wer value: 31.38 --- # Wav2Vec2-Large-XLSR-53-Hakha-Chin Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hakha Chin using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "cnh", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh/") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "cnh", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\/]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 31.38 % ## Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1pejk9gv9vMcUOjyVQ_vsV2ngW4NiWLWy?usp=sharing).
ChaitanyaU/FineTuneLM	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	"2021-03-27T16:56:10Z"	--- language: eo datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Esperanto by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice eo type: common_voice args: eo metrics: - name: Test WER type: wer value: 10.13 --- # Wav2Vec2-Large-XLSR-53-Esperanto Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "eo", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import jiwer def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(targets): chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end]) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] start += chunk_size end += chunk_size return float(S + D + I) / float(H + S + D) test_dataset = load_dataset("common_voice", "eo", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the ISO codes of [this](https://huggingface.co/languages) site. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model.to("cuda") chars_to_ignore_regex = """[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�\\\\\\\\„\\\\\\\\«\\\\\\\\(\\\\\\\\»\\\\\\\\)\\\\\\\\’\\\\\\\\']""" resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace('—',' ').replace('–',' ') speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=5000))) ``` Test Result: 10.13 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-esperanto-asr-with-transformers-final.ipynb).
Chakita/Friends	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	null	--- language: gu datasets: - openslr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Gujarati by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR gu type: openslr metrics: - name: Test WER type: wer value: 23.55 --- # Wav2Vec2-Large-XLSR-53-Gujarati Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Gujarati using the [OpenSLR SLR78](http://openslr.org/78/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Gujarati `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. # For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input. # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset_eval = test_dataset_eval.map(speech_file_to_array_fn) inputs = processor(test_dataset_eval["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset_eval["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…\'\_\’]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 23.55 % ## Training 90% of the OpenSLR Gujarati Male+Female dataset was used for training, after removing few examples that contained Roman characters. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1fRQlgl4EPR4qKGScgza3MpWgbL5BeWtn?usp=sharing).
Chakita/KNUBert	[ "pytorch", "tensorboard", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	20	"2021-03-24T19:47:37Z"	--- language: hu datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Hungarian by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice hu type: common_voice args: hu metrics: - name: Test WER type: wer value: 46.75 --- # Wav2Vec2-Large-XLSR-53-Hungarian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "hu", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "hu", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 46.75 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-hungarian-asr.ipynb). The notebook containing the code used for evaluation can be found [here](https://colab.research.google.com/drive/1esYvWS6IkTQFfRqi_b6lAJEycuecInHE?usp=sharing).
Chakita/KROBERT	[ "pytorch", "roberta", "fill-mask", "transformers", "masked-lm", "fill-in-the-blanks", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- language: ia datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Interlingua by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ia type: common_voice args: ia metrics: - name: Test WER type: wer value: 25.09 --- # Wav2Vec2-Large-XLSR-53-Interlingua Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Interlingua using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ia", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Odia test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ia", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 25.09 % ## Training The Common Voice `train` and `validation` datasets were used for training for 4000 steps due to GPU timeout. The results are based on the 4000 steps checkpoint. There is a good chance that full training will lead to better results. The colab notebook used can be found [here](https://colab.research.google.com/drive/1nbqvVwS8DTNrCzzh3vgrN55qxgoqbita?usp=sharing) and the evaluation can be found [here](https://colab.research.google.com/drive/18pCWBwNNUMUYV1FiqT_0EsTbCfwwe7ms?usp=sharing).
Chakita/Kalbert	[ "pytorch", "tensorboard", "albert", "fill-mask", "transformers", "generated_from_trainer", "license:mit", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- language: it datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Italian by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice it type: common_voice args: it metrics: - name: Test WER type: wer value: 11.49 --- # Wav2Vec2-Large-XLSR-53-Italian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Italian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "it", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import unicodedata import jiwer def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(targets): chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end]) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] start += chunk_size end += chunk_size return float(S + D + I) / float(H + S + D) allowed_characters = [ " ", "'", 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'à', 'á', 'è', 'é', 'ì', 'í', 'ò', 'ó', 'ù', 'ú', ] def remove_accents(input_str): if input_str in allowed_characters: return input_str if input_str == 'ø': return 'o' elif input_str=='ß' or input_str =='ß': return 'b' elif input_str=='ё': return 'e' elif input_str=='đ': return 'd' nfkd_form = unicodedata.normalize('NFKD', input_str) only_ascii = nfkd_form.encode('ASCII', 'ignore').decode() if only_ascii is None or only_ascii=='': return input_str else: return only_ascii def fix_accents(sentence): new_sentence='' for char in sentence: new_sentence+=remove_accents(char) return new_sentence test_dataset = load_dataset("common_voice", "it", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) chars_to_remove= [",", "?", ".", "!", "-", ";", ":", '""', "%", '"', "�",'ʿ','“','”','(','=','`','_','+','«','<','>','~','…','«','»','–','\[','\]','°','̇','´','ʾ','„','̇','̇','̇','¡'] # All extra characters chars_to_remove_regex = f'[{"".join(chars_to_remove)}]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower().replace('‘',"'").replace('ʻ',"'").replace('ʼ',"'").replace('’',"'").replace('ʹ',"''").replace('̇','') batch["sentence"] = fix_accents(batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=5000))) ``` Test Result: 11.49 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-italian-asr-with-transformers_final.ipynb).
Chakita/KannadaBERT	[ "pytorch", "roberta", "fill-mask", "transformers", "masked-lm", "fill-in-the-blanks", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	"2021-03-25T20:55:44Z"	--- language: mr datasets: - interspeech_2021_asr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Marathi 2 by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: InterSpeech 2021 ASR mr type: interspeech_2021_asr metrics: - name: Test WER type: wer value: 14.53 --- # Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using a part of the [InterSpeech 2021 Marathi](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") resampler = torchaudio.transforms.Resample(8_000, 16_000) # The original data was with 8,000 sampling rate. You can change it according to your input. # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the test set of the Marathi data on InterSpeech-2021. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(8_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 19.98 % (555 examples from test set were used for evaluation) Test Result on 10% of OpenSLR74 data: 64.64 % ## Training 5000 examples of the InterSpeech Marathi dataset were used for training. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1sIwGOLJPQqhKm_wVZDkzRuoJqAEgArFr?usp=sharing).
Chakita/gpt2_mwp	[ "pytorch", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	6	"2021-03-26T00:56:33Z"	--- language: mr datasets: - openslr - interspeech_2021_asr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR mr, InterSpeech 2021 ASR mr type: openslr, interspeech_2021_asr metrics: - name: Test WER type: wer value: 19.05 --- # Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset and [InterSpeech 2021](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) Marathi datasets. Note that this data OpenSLR contains only female voices. Please keep this in mind before using the model for your task. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `text` and `audio_path` fields: ```python import torch import torchaudio import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["audio_path"]) batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary return batch test_data= test_data.map(speech_file_to_array_fn) inputs = processor(test_data["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_data["text"][:2]) ``` ## Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ```python import torch import torchaudio import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]' # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower() speech_array, sampling_rate = torchaudio.load(batch["audio_path"]) batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) return batch test_data= test_data.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_data.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"]))) ``` Test Result: 19.05 % (157+157 examples) Test Result on OpenSLR test: 14.15 % (157 examples) Test Results on InterSpeech test: 27.14 % (157 examples) ## Training 1412 examples of the OpenSLR Marathi dataset and 1412 examples of InterSpeech 2021 Marathi ASR dataset were used for training. For testing, 157 examples from each were used. The colab notebook used for training and evaluation can be found [here](https://colab.research.google.com/drive/15fUhb4bUFFGJyNLr-_alvPxVX4w0YXRu?usp=sharing).
Chalponkey/DialoGPT-small-Barry	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	11	null	--- language: mr datasets: - openslr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR mr type: openslr metrics: - name: Test WER type: wer value: 14.53 --- # Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, although it works very well for male voice too. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input. # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 14.53 % ## Training 90% of the OpenSLR Marathi dataset was used for training. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1_BbLyLqDUsXG3RpSULfLRjC6UY3RjwME?usp=sharing).
Champion/test_upload_vox2_wavlm_epoch8	[ "sidekit", "audio" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: or datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Odia by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice or type: common_voice args: or metrics: - name: Test WER type: wer value: 52.64 --- # Wav2Vec2-Large-XLSR-53-Odia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Odia using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "or", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Odia test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "or", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…\'\_\’\।\\|]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 52.64 % ## Training The Common Voice `train` and `validation` datasets were used for training.The colab notebook used can be found [here](https://colab.research.google.com/drive/1s8DrwgB5y4Z7xXIrPXo1rQA5_1OZ8WD5?usp=sharing).
Chan/distilgpt2-finetuned-wikitext2	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	"2021-03-23T13:56:09Z"	--- language: pt datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Portugese by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice pt type: common_voice args: pt metrics: - name: Test WER type: wer value: 17.22 --- # Wav2Vec2-Large-XLSR-53-Portuguese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "pt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pt", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\;\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 17.22 % ## Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://github.com/jqueguiner/wav2vec2-sprint/blob/main/run_common_voice.py). The parameters passed were: ```bash #!/usr/bin/env bash python run_common_voice.py \ --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \ --dataset_config_name="pt" \ --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \ --cache_dir=/workspace/data \ --overwrite_output_dir \ --num_train_epochs="30" \ --per_device_train_batch_size="32" \ --per_device_eval_batch_size="32" \ --evaluation_strategy="steps" \ --learning_rate="3e-4" \ --warmup_steps="500" \ --fp16 \ --freeze_feature_extractor \ --save_steps="500" \ --eval_steps="500" \ --save_total_limit="1" \ --logging_steps="500" \ --group_by_length \ --feat_proj_dropout="0.0" \ --layerdrop="0.1" \ --gradient_checkpointing \ --do_train --do_eval \ ``` Notebook containing the evaluation can be found [here](https://colab.research.google.com/drive/14e-zNK_5pm8EMY9EbeZerpHx7WsGycqG?usp=sharing).
Chan/distilroberta-base-finetuned-wikitext2	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	"2021-03-27T14:23:51Z"	--- language: rm-sursilv datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Romansh Sursilvan by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice rm-sursilv type: common_voice args: rm-sursilv metrics: - name: Test WER type: wer value: 25.16 --- # Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Sursilvan using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "rm-sursilv", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "rm-sursilv", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\…\\«\\»\\–]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Test Result: 25.16 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://colab.research.google.com/drive/1dpZr_GzRowCciUbzM3GnW04TNKnB7vrP?usp=sharing).
Cheapestmedsshop/Buymodafinilus	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	"2021-07-28T12:51:00Z"	--- language: el --- # GreekSocialBERT ## Model description A Greek language model based on [GreekBERT](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) ## Training data The training data is a corpus of 458,293 documents collected from Greek social media accounts. The training corpus has been collected and provided by [Palo LTD](http://www.paloservices.com/) ## Eval results ### BibTeX entry and citation info ```bibtex @Article{info12080331, AUTHOR = {Alexandridis, Georgios and Varlamis, Iraklis and Korovesis, Konstantinos and Caridakis, George and Tsantilas, Panagiotis}, TITLE = {A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media}, JOURNAL = {Information}, VOLUME = {12}, YEAR = {2021}, NUMBER = {8}, ARTICLE-NUMBER = {331}, URL = {https://www.mdpi.com/2078-2489/12/8/331}, ISSN = {2078-2489}, DOI = {10.3390/info12080331} } ```
Cheatham/xlm-roberta-base-finetuned	[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "XLMRobertaForSequenceClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	20	null	--- language: el --- # PaloBERT ## Model description A Greek language model based on [RoBERTa](https://arxiv.org/abs/1907.11692) ## Training data The training data is a corpus of 458,293 documents collected from Greek social media accounts. It also contains a GTP-2 tokenizer trained from scratch on the same corpus. The training corpus has been collected and provided by [Palo LTD](http://www.paloservices.com/) ## Eval results ### BibTeX entry and citation info ```bibtex @Article{info12080331, AUTHOR = {Alexandridis, Georgios and Varlamis, Iraklis and Korovesis, Konstantinos and Caridakis, George and Tsantilas, Panagiotis}, TITLE = {A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media}, JOURNAL = {Information}, VOLUME = {12}, YEAR = {2021}, NUMBER = {8}, ARTICLE-NUMBER = {331}, URL = {https://www.mdpi.com/2078-2489/12/8/331}, ISSN = {2078-2489}, DOI = {10.3390/info12080331} } ```
Check/vaw2tmp	[ "tensorboard" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	https://dl.fbaipublicfiles.com/avhubert/model/lrs3_vox/vsr/base_vox_433h.pt
D3xter1922/electra-base-discriminator-finetuned-mnli	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	A fake news detector using RoBERTa. Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset Training involved using hyperparameter search with 10 trials.
DJSammy/bert-base-danish-uncased_BotXO-ai	[ "pytorch", "jax", "da", "dataset:common_crawl", "dataset:wikipedia", "transformers", "bert", "masked-lm", "license:cc-by-4.0", "fill-mask" ]	fill-mask	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	14	null	This repository belongs to TransportersBERT from ActTrans publication. Taju, Semmy Wellem, Syed Muazzam Ali Shah, and Yu-Yen Ou. “ActTRANS: Functional Classification in Active Transport Proteins Based on Transfer Learning and Contextual Representations.” Computational Biology and Chemistry 93 (August 1, 2021): 107537. https://doi.org/10.1016/j.compbiolchem.2021.107537.
DTAI-KULeuven/robbertje-1-gb-shuffled	[ "pytorch", "roberta", "fill-mask", "nl", "dataset:oscar", "dataset:oscar (NL)", "dataset:dbrd", "dataset:lassy-ud", "dataset:europarl-mono", "dataset:conll2002", "arxiv:2101.05716", "transformers", "Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje", "license:mit", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- language: bn tags: - text-generation widget: - text: তোমাকে দেখেছি আমার হৃদয় মাঝে --- # Robi Kobi ### Created by [Ritobrata Ghosh](https://ghosh-r.github.io) A model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore. This model is fine-tuned on 1,400+ poems written by Rabindranath Tagore. This model leverages the [Bangla GPT-2](https://huggingface.co/ghosh-r/bangla-gpt2) pretrained model, trained on mc4-Bengali dataset.
Daiki/scibert_scivocab_uncased-finetuned-cola	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: - ro license: apache-2.0 tags: - automatic-speech-recognition - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 - gigant/romanian_speech_synthesis_0_8_1 model-index: - name: wav2vec2-ro-300m_01 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event type: speech-recognition-community-v2/dev_data args: ro metrics: - name: Dev WER (without LM) type: wer value: 46.99 - name: Dev CER (without LM) type: cer value: 16.04 - name: Dev WER (with LM) type: wer value: 38.63 - name: Dev CER (with LM) type: cer value: 14.52 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice type: mozilla-foundation/common_voice_8_0 args: ro metrics: - name: Test WER (without LM) type: wer value: 11.73 - name: Test CER (without LM) type: cer value: 2.93 - name: Test WER (with LM) type: wer value: 7.31 - name: Test CER (with LM) type: cer value: 2.17 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: ro metrics: - name: Test WER type: wer value: 43.23 --- You can test this model online with the [Space for Romanian Speech Recognition](https://huggingface.co/spaces/gigant/romanian-speech-recognition) The model ranked TOP-1 on Romanian Speech Recognition during HuggingFace's Robust Speech Challenge : * [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench) * [Speech Challenge Leaderboard](https://huggingface.co/spaces/speech-recognition-community-v2/FinalLeaderboard) # Romanian Wav2Vec2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) dataset, with extra training data from [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) dataset. Without the 5-gram Language Model optimization, it achieves the following results on the evaluation set (Common Voice 8.0, Romanian subset, test split): - Loss: 0.1553 - Wer: 0.1174 - Cer: 0.0294 ## Model description The architecture is based on [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) with a speech recognition CTC head and an added 5-gram language model (using [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) and [kenlm](https://github.com/kpu/kenlm)) trained on the [Romanian Corpora Parliament](gigant/ro_corpora_parliament_processed) dataset. Those libraries are needed in order for the language model-boosted decoder to work. ## Intended uses & limitations The model is made for speech recognition in Romanian from audio clips sampled at 16kHz. The predicted text is lowercased and does not contain any punctuation. ## How to use Make sure you have installed the correct dependencies for the language model-boosted version to work. You can just run this command to install the `kenlm` and `pyctcdecode` libraries : ```pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode``` With the framework `transformers` you can load the model with the following code : ``` from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("gigant/romanian-wav2vec2") model = AutoModelForCTC.from_pretrained("gigant/romanian-wav2vec2") ``` Or, if you want to test the model, you can load the automatic speech recognition pipeline from `transformers` with : ``` from transformers import pipeline asr = pipeline("automatic-speech-recognition", model="gigant/romanian-wav2vec2") ``` ## Example use with the `datasets` library First, you need to load your data We will use the [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) dataset in this example. ``` from datasets import load_dataset dataset = load_dataset("gigant/romanian_speech_synthesis_0_8_1") ``` You can listen to the samples with the `IPython.display` library : ``` from IPython.display import Audio i = 0 sample = dataset["train"][i] Audio(sample["audio"]["array"], rate = sample["audio"]["sampling_rate"]) ``` The model is trained to work with audio sampled at 16kHz, so if the sampling rate of the audio in the dataset is different, we will have to resample it. In the example, the audio is sampled at 48kHz. We can see this by checking `dataset["train"][0]["audio"]["sampling_rate"]` The following code resample the audio using the `torchaudio` library : ``` import torchaudio import torch i = 0 audio = sample["audio"]["array"] rate = sample["audio"]["sampling_rate"] resampler = torchaudio.transforms.Resample(rate, 16_000) audio_16 = resampler(torch.Tensor(audio)).numpy() ``` To listen to the resampled sample : ``` Audio(audio_16, rate=16000) ``` Know you can get the model prediction by running ``` predicted_text = asr(audio_16) ground_truth = dataset["train"][i]["sentence"] print(f"Predicted text : {predicted_text}") print(f"Ground truth : {ground_truth}") ``` ## Training and evaluation data Training data : - [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : train + validation + other splits - [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) : train + test splits Evaluation data : - [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : test split ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\| \| 2.9272 \| 0.78 \| 500 \| 0.7603 \| 0.7734 \| 0.2355 \| \| 0.6157 \| 1.55 \| 1000 \| 0.4003 \| 0.4866 \| 0.1247 \| \| 0.4452 \| 2.33 \| 1500 \| 0.2960 \| 0.3689 \| 0.0910 \| \| 0.3631 \| 3.11 \| 2000 \| 0.2580 \| 0.3205 \| 0.0796 \| \| 0.3153 \| 3.88 \| 2500 \| 0.2465 \| 0.2977 \| 0.0747 \| \| 0.2795 \| 4.66 \| 3000 \| 0.2274 \| 0.2789 \| 0.0694 \| \| 0.2615 \| 5.43 \| 3500 \| 0.2277 \| 0.2685 \| 0.0675 \| \| 0.2389 \| 6.21 \| 4000 \| 0.2135 \| 0.2518 \| 0.0627 \| \| 0.2229 \| 6.99 \| 4500 \| 0.2054 \| 0.2449 \| 0.0614 \| \| 0.2067 \| 7.76 \| 5000 \| 0.2096 \| 0.2378 \| 0.0597 \| \| 0.1977 \| 8.54 \| 5500 \| 0.2042 \| 0.2387 \| 0.0600 \| \| 0.1896 \| 9.32 \| 6000 \| 0.2110 \| 0.2383 \| 0.0595 \| \| 0.1801 \| 10.09 \| 6500 \| 0.1909 \| 0.2165 \| 0.0548 \| \| 0.174 \| 10.87 \| 7000 \| 0.1883 \| 0.2206 \| 0.0559 \| \| 0.1685 \| 11.65 \| 7500 \| 0.1848 \| 0.2097 \| 0.0528 \| \| 0.1591 \| 12.42 \| 8000 \| 0.1851 \| 0.2039 \| 0.0514 \| \| 0.1537 \| 13.2 \| 8500 \| 0.1881 \| 0.2065 \| 0.0518 \| \| 0.1504 \| 13.97 \| 9000 \| 0.1840 \| 0.1972 \| 0.0499 \| \| 0.145 \| 14.75 \| 9500 \| 0.1845 \| 0.2029 \| 0.0517 \| \| 0.1417 \| 15.53 \| 10000 \| 0.1884 \| 0.2003 \| 0.0507 \| \| 0.1364 \| 16.3 \| 10500 \| 0.2010 \| 0.2037 \| 0.0517 \| \| 0.1331 \| 17.08 \| 11000 \| 0.1838 \| 0.1923 \| 0.0483 \| \| 0.129 \| 17.86 \| 11500 \| 0.1818 \| 0.1922 \| 0.0489 \| \| 0.1198 \| 18.63 \| 12000 \| 0.1760 \| 0.1861 \| 0.0465 \| \| 0.1203 \| 19.41 \| 12500 \| 0.1686 \| 0.1839 \| 0.0465 \| \| 0.1225 \| 20.19 \| 13000 \| 0.1828 \| 0.1920 \| 0.0479 \| \| 0.1145 \| 20.96 \| 13500 \| 0.1673 \| 0.1784 \| 0.0446 \| \| 0.1053 \| 21.74 \| 14000 \| 0.1802 \| 0.1810 \| 0.0456 \| \| 0.1071 \| 22.51 \| 14500 \| 0.1769 \| 0.1775 \| 0.0444 \| \| 0.1053 \| 23.29 \| 15000 \| 0.1920 \| 0.1783 \| 0.0457 \| \| 0.1024 \| 24.07 \| 15500 \| 0.1904 \| 0.1775 \| 0.0446 \| \| 0.0987 \| 24.84 \| 16000 \| 0.1793 \| 0.1762 \| 0.0446 \| \| 0.0949 \| 25.62 \| 16500 \| 0.1801 \| 0.1766 \| 0.0443 \| \| 0.0942 \| 26.4 \| 17000 \| 0.1731 \| 0.1659 \| 0.0423 \| \| 0.0906 \| 27.17 \| 17500 \| 0.1776 \| 0.1698 \| 0.0424 \| \| 0.0861 \| 27.95 \| 18000 \| 0.1716 \| 0.1600 \| 0.0406 \| \| 0.0851 \| 28.73 \| 18500 \| 0.1662 \| 0.1630 \| 0.0410 \| \| 0.0844 \| 29.5 \| 19000 \| 0.1671 \| 0.1572 \| 0.0393 \| \| 0.0792 \| 30.28 \| 19500 \| 0.1768 \| 0.1599 \| 0.0407 \| \| 0.0798 \| 31.06 \| 20000 \| 0.1732 \| 0.1558 \| 0.0394 \| \| 0.0779 \| 31.83 \| 20500 \| 0.1694 \| 0.1544 \| 0.0388 \| \| 0.0718 \| 32.61 \| 21000 \| 0.1709 \| 0.1578 \| 0.0399 \| \| 0.0732 \| 33.38 \| 21500 \| 0.1697 \| 0.1523 \| 0.0391 \| \| 0.0708 \| 34.16 \| 22000 \| 0.1616 \| 0.1474 \| 0.0375 \| \| 0.0678 \| 34.94 \| 22500 \| 0.1698 \| 0.1474 \| 0.0375 \| \| 0.0642 \| 35.71 \| 23000 \| 0.1681 \| 0.1459 \| 0.0369 \| \| 0.0661 \| 36.49 \| 23500 \| 0.1612 \| 0.1411 \| 0.0357 \| \| 0.0629 \| 37.27 \| 24000 \| 0.1662 \| 0.1414 \| 0.0355 \| \| 0.0587 \| 38.04 \| 24500 \| 0.1659 \| 0.1408 \| 0.0351 \| \| 0.0581 \| 38.82 \| 25000 \| 0.1612 \| 0.1382 \| 0.0352 \| \| 0.0556 \| 39.6 \| 25500 \| 0.1647 \| 0.1376 \| 0.0345 \| \| 0.0543 \| 40.37 \| 26000 \| 0.1658 \| 0.1335 \| 0.0337 \| \| 0.052 \| 41.15 \| 26500 \| 0.1716 \| 0.1369 \| 0.0343 \| \| 0.0513 \| 41.92 \| 27000 \| 0.1600 \| 0.1317 \| 0.0330 \| \| 0.0491 \| 42.7 \| 27500 \| 0.1671 \| 0.1311 \| 0.0328 \| \| 0.0463 \| 43.48 \| 28000 \| 0.1613 \| 0.1289 \| 0.0324 \| \| 0.0468 \| 44.25 \| 28500 \| 0.1599 \| 0.1260 \| 0.0315 \| \| 0.0435 \| 45.03 \| 29000 \| 0.1556 \| 0.1232 \| 0.0308 \| \| 0.043 \| 45.81 \| 29500 \| 0.1588 \| 0.1240 \| 0.0309 \| \| 0.0421 \| 46.58 \| 30000 \| 0.1567 \| 0.1217 \| 0.0308 \| \| 0.04 \| 47.36 \| 30500 \| 0.1533 \| 0.1198 \| 0.0302 \| \| 0.0389 \| 48.14 \| 31000 \| 0.1582 \| 0.1185 \| 0.0297 \| \| 0.0387 \| 48.91 \| 31500 \| 0.1576 \| 0.1187 \| 0.0297 \| \| 0.0376 \| 49.69 \| 32000 \| 0.1560 \| 0.1182 \| 0.0295 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.0 - pyctcdecode 0.3.0 - kenlm

CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-egy

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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https://www.nace.org/network/members/profile?UserKey=461a690a-bff6-4e4c-be63-ea8e39264459 https://www.nace.org/network/members/profile?UserKey=b4a6a66a-fb8a-4f2b-8af9-04f003ad9d46 https://www.nace.org/network/members/profile?UserKey=24544ab2-551d-42aa-adbe-7a1c1d68fd9c https://www.nace.org/network/members/profile?UserKey=3e8035d5-056a-482d-9010-9883e5990f4a https://www.nace.org/network/members/profile?UserKey=d7241c69-28c4-4146-a077-a00cc2c9ccf5 https://www.nace.org/network/members/profile?UserKey=2c58c2fb-13a4-4e5a-b044-f467bb295d83 https://www.nace.org/network/members/profile?UserKey=dd8a290c-e53a-4b56-9a17-d35dbcb6b8bd https://www.nace.org/network/members/profile?UserKey=0e96a1af-91f4-496a-af02-6d753a1bbded

CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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https://ragbrai.com/groups/hd-movie-watch-french-exit-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-nobody-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-voyagers-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-godzilla-vs-kong-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-raya-and-the-last-dragon-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-mortal-kombat-2021-full-movie-online-for-free/ https://ragbrai.com/groups/hd-movie-watch-the-father-2021-full-movie-online-for-free/

CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer intermediate model (B6-6-6 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. **Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `intermediate` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/intermediate-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/intermediate-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]

text-classification

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--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer intermediate model (B6-6-6 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate") model = FunneModel.from_pretrained("funnel-transformer/intermediate") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/intermediate") model = TFFunnelModel.from_pretrained("funnel-transformer/intermediatesmall") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-ca

[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

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580

null

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer large model (B8-8-8 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. **Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `large` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/large-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/large-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-ner

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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42

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--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer large model (B8-8-8 with decoder) Pretrained model on English language using a similar objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large") model = FunneModel.from_pretrained("funnel-transformer/large") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large") model = TFFunnelModel.from_pretrained("funnel-transformer/large") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-poetry

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:1905.05700", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

37

null

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer medium model (B6-3x2-3x2 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. **Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `medium` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/medium-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/medium-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-pos-egy

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

32

null

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer medium model (B6-3x2-3x2 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium") model = FunneModel.from_pretrained("funnel-transformer/medium") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/medium") model = TFFunnelModel.from_pretrained("funnel-transformer/medium") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

54

null

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer small model (B4-4-4 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. **Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `small` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/small-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/small-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

27

null

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer small model (B4-4-4 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small") model = FunneModel.from_pretrained("funnel-transformer/small") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/small") model = TFFunnelModel.from_pretrained("funnel-transformer/small") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "has_space" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

19,850

null

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer xlarge model (B10-10-10 without decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. **Note:** This model does not contain the decoder, so it ouputs hidden states that have a sequence length of one fourth of the inputs. It's good to use for tasks requiring a summary of the sentence (like sentence classification) but not if you need one input per initial token. You should use the `xlarge` model in that case. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge-base") model = FunnelBaseModel.from_pretrained("funnel-transformer/xlarge-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelBaseModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge-base") model = TFFunnelBaseModel.from_pretrained("funnel-transformer/xlarge-base") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-da

[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

449

null

--- language: en license: apache-2.0 datasets: - bookcorpus - wikipedia - gigaword --- # Funnel Transformer xlarge model (B10-10-10 with decoder) Pretrained model on English language using a similar objective objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.com/laiguokun/Funnel-Transformer). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, a small language model corrupts the input texts and serves as a generator of inputs for this model, and the pretraining objective is to predict which token is an original and which one has been replaced, a bit like a GAN training. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Intended uses & limitations You can use the raw model to extract a vector representation of a given text, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=funnel-transformer) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge") model = FunneModel.from_pretrained("funnel-transformer/xlarge") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import FunnelTokenizer, TFFunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/xlarge") model = TFFunnelModel.from_pretrained("funnel-transformer/xlarge") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Training data The BERT model was pretrained on: - [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books, - [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers), - [Clue Web](https://lemurproject.org/clueweb12/), a dataset of 733,019,372 English web pages, - [GigaWord](https://catalog.ldc.upenn.edu/LDC2011T07), an archive of newswire text data, - [Common Crawl](https://commoncrawl.org/), a dataset of raw web pages. ### BibTeX entry and citation info ```bibtex @misc{dai2020funneltransformer, title={Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing}, author={Zihang Dai and Guokun Lai and Yiming Yang and Quoc V. Le}, year={2020}, eprint={2006.03236}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```

CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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62

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-finetuned-bbc-headline results: [] ---  # t5-base-finetuned-bbc-headline This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 167 | 2.2978 | 31.8313 | 10.3824 | 29.6182 | 29.4336 | 10.3153 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3

CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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132

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-base-finetuned-bbc results: [] ---  # t5-base-finetuned-bbc This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 334 | 0.1500 | 24.5024 | 21.4979 | 24.0227 | 24.0303 | 19.0 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3

CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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1,862

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-bbc-headline results: [] ---  # t5-small-finetuned-bbc-headline This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 167 | 3.6454 | 22.4311 | 5.9878 | 20.118 | 20.482 | 18.9009 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3

CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

855

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-bbc results: [] ---  # t5-small-finetuned-bbc This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3238 - Rouge1: 21.2266 - Rouge2: 16.0927 - Rougel: 19.6785 - Rougelsum: 19.8849 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.4882 | 1.0 | 1001 | 0.3238 | 21.2266 | 16.0927 | 19.6785 | 19.8849 | 19.0 | ### Framework versions - Transformers 4.12.0 - Pytorch 1.10.0 - Datasets 1.14.0 - Tokenizers 0.10.3

CAMeL-Lab/bert-base-arabic-camelbert-mix

[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "Arabic", "Dialect", "Egyptian", "Gulf", "Levantine", "Classical Arabic", "MSA", "Modern Standard Arabic", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

20,880

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum model-index: - name: t5-small-finetuned-xsum results: [] ---  # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 128 | 2.9003 | 19.4784 | 2.8529 | 14.7786 | 15.0614 | 18.9825 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.12.1 - Tokenizers 0.10.3

CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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52

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-wikitext2 results: [] ---  # bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8575 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.0964 | 1.0 | 2346 | 7.0532 | | 6.9055 | 2.0 | 4692 | 6.8710 | | 6.8574 | 3.0 | 7038 | 6.8917 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

21

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5543972545286807 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8273 - Matthews Correlation: 0.5544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5256 | 1.0 | 535 | 0.5419 | 0.4248 | | 0.3486 | 2.0 | 1070 | 0.5187 | 0.4999 | | 0.2406 | 3.0 | 1605 | 0.6580 | 0.5054 | | 0.1692 | 4.0 | 2140 | 0.7455 | 0.5403 | | 0.1343 | 5.0 | 2675 | 0.8273 | 0.5544 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

133

null

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] ---  # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.1112 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.5571 | 1.0 | 2249 | 6.4684 | | 6.1921 | 2.0 | 4498 | 6.1984 | | 6.0016 | 3.0 | 6747 | 6.1112 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

CBreit00/DialoGPT_small_Rick

[]

null

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0

null

--- language: - es license: apache-2.0 tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-common_voice-es-demo results: [] ---  # wav2vec2-common_voice-es-demo This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - ES dataset. It achieves the following results on the evaluation set: - Loss: 0.1788 - Wer: 1.0239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 15.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.02 | 100 | 6.6465 | 1.0 | | No log | 0.04 | 200 | 3.0150 | 1.0 | | No log | 0.05 | 300 | 2.8622 | 1.0003 | | No log | 0.07 | 400 | 0.9506 | 0.9771 | | 5.1598 | 0.09 | 500 | 0.4883 | 1.0009 | | 5.1598 | 0.11 | 600 | 0.3893 | 1.0203 | | 5.1598 | 0.13 | 700 | 0.3417 | 1.0283 | | 5.1598 | 0.14 | 800 | 0.3352 | 1.0335 | | 5.1598 | 0.16 | 900 | 0.2987 | 1.0168 | | 0.3671 | 0.18 | 1000 | 0.2921 | 1.0159 | | 0.3671 | 0.2 | 1100 | 0.2770 | 1.0096 | | 0.3671 | 0.22 | 1200 | 0.2790 | 1.0398 | | 0.3671 | 0.24 | 1300 | 0.2659 | 1.0190 | | 0.3671 | 0.25 | 1400 | 0.2657 | 1.0528 | | 0.289 | 0.27 | 1500 | 0.2556 | 1.0301 | | 0.289 | 0.29 | 1600 | 0.2514 | 1.0193 | | 0.289 | 0.31 | 1700 | 0.2708 | 1.0699 | | 0.289 | 0.33 | 1800 | 0.2455 | 1.0723 | | 0.289 | 0.34 | 1900 | 0.2456 | 1.0100 | | 0.271 | 0.36 | 2000 | 0.2338 | 1.0533 | | 0.271 | 0.38 | 2100 | 0.2479 | 1.0128 | | 0.271 | 0.4 | 2200 | 0.2483 | 1.0386 | | 0.271 | 0.42 | 2300 | 0.2436 | 1.0528 | | 0.271 | 0.43 | 2400 | 0.2382 | 1.0476 | | 0.2634 | 0.45 | 2500 | 0.2329 | 1.0680 | | 0.2634 | 0.47 | 2600 | 0.2433 | 1.0581 | | 0.2634 | 0.49 | 2700 | 0.2354 | 1.0641 | | 0.2634 | 0.51 | 2800 | 0.2318 | 1.0504 | | 0.2634 | 0.52 | 2900 | 0.2325 | 1.0500 | | 0.2522 | 0.54 | 3000 | 0.2344 | 1.0380 | | 0.2522 | 0.56 | 3100 | 0.2244 | 1.0663 | | 0.2522 | 0.58 | 3200 | 0.2340 | 1.0647 | | 0.2522 | 0.6 | 3300 | 0.2288 | 1.0538 | | 0.2522 | 0.61 | 3400 | 0.2212 | 1.0614 | | 0.2468 | 0.63 | 3500 | 0.2487 | 1.0557 | | 0.2468 | 0.65 | 3600 | 0.2330 | 1.0510 | | 0.2468 | 0.67 | 3700 | 0.2308 | 1.0506 | | 0.2468 | 0.69 | 3800 | 0.2320 | 1.0451 | | 0.2468 | 0.71 | 3900 | 0.2261 | 1.0701 | | 0.2505 | 0.72 | 4000 | 0.2281 | 1.0713 | | 0.2505 | 0.74 | 4100 | 0.2277 | 1.0741 | | 0.2505 | 0.76 | 4200 | 0.2253 | 1.0814 | | 0.2505 | 0.78 | 4300 | 0.2215 | 1.0437 | | 0.2505 | 0.8 | 4400 | 0.2220 | 1.0557 | | 0.2434 | 0.81 | 4500 | 0.2184 | 1.0533 | | 0.2434 | 0.83 | 4600 | 0.2222 | 1.0819 | | 0.2434 | 0.85 | 4700 | 0.2162 | 1.0238 | | 0.2434 | 0.87 | 4800 | 0.2132 | 1.0457 | | 0.2434 | 0.89 | 4900 | 0.2068 | 1.0611 | | 0.2347 | 0.9 | 5000 | 0.2166 | 1.0332 | | 0.2347 | 0.92 | 5100 | 0.2087 | 1.0433 | | 0.2347 | 0.94 | 5200 | 0.2100 | 1.0292 | | 0.2347 | 0.96 | 5300 | 0.2067 | 1.0734 | | 0.2347 | 0.98 | 5400 | 0.2148 | 1.0279 | | 0.2333 | 0.99 | 5500 | 0.2125 | 1.0277 | | 0.2333 | 1.01 | 5600 | 0.2054 | 1.0453 | | 0.2333 | 1.03 | 5700 | 0.2091 | 1.0557 | | 0.2333 | 1.05 | 5800 | 0.2086 | 1.0239 | | 0.2333 | 1.07 | 5900 | 0.2051 | 1.0645 | | 0.2087 | 1.09 | 6000 | 0.2103 | 1.0240 | | 0.2087 | 1.1 | 6100 | 0.2145 | 1.0197 | | 0.2087 | 1.12 | 6200 | 0.2136 | 1.0248 | | 0.2087 | 1.14 | 6300 | 0.2045 | 1.0443 | | 0.2087 | 1.16 | 6400 | 0.2089 | 1.0397 | | 0.2013 | 1.18 | 6500 | 0.2012 | 1.0654 | | 0.2013 | 1.19 | 6600 | 0.2054 | 1.0414 | | 0.2013 | 1.21 | 6700 | 0.2081 | 1.0632 | | 0.2013 | 1.23 | 6800 | 0.2104 | 1.0190 | | 0.2013 | 1.25 | 6900 | 0.2045 | 1.0813 | | 0.2092 | 1.27 | 7000 | 0.2096 | 1.0751 | | 0.2092 | 1.28 | 7100 | 0.2103 | 1.0328 | | 0.2092 | 1.3 | 7200 | 0.2044 | 1.0011 | | 0.2092 | 1.32 | 7300 | 0.2089 | 1.0260 | | 0.2092 | 1.34 | 7400 | 0.2063 | 1.0551 | | 0.2076 | 1.36 | 7500 | 0.2029 | 1.0075 | | 0.2076 | 1.37 | 7600 | 0.2040 | 1.0528 | | 0.2076 | 1.39 | 7700 | 0.2075 | 1.0398 | | 0.2076 | 1.41 | 7800 | 0.2023 | 1.0231 | | 0.2076 | 1.43 | 7900 | 0.2049 | 1.0318 | | 0.2028 | 1.45 | 8000 | 0.2072 | 1.0763 | | 0.2028 | 1.47 | 8100 | 0.2075 | 1.0762 | | 0.2028 | 1.48 | 8200 | 0.2052 | 1.0838 | | 0.2028 | 1.5 | 8300 | 0.2053 | 1.0407 | | 0.2028 | 1.52 | 8400 | 0.2066 | 1.0266 | | 0.2025 | 1.54 | 8500 | 0.2037 | 1.0628 | | 0.2025 | 1.56 | 8600 | 0.2010 | 1.0351 | | 0.2025 | 1.57 | 8700 | 0.1961 | 1.0812 | | 0.2025 | 1.59 | 8800 | 0.1963 | 1.0868 | | 0.2025 | 1.61 | 8900 | 0.2022 | 1.0710 | | 0.1997 | 1.63 | 9000 | 0.2051 | 1.0764 | | 0.1997 | 1.65 | 9100 | 0.1987 | 1.0581 | | 0.1997 | 1.66 | 9200 | 0.2051 | 1.0611 | | 0.1997 | 1.68 | 9300 | 0.1999 | 1.0808 | | 0.1997 | 1.7 | 9400 | 0.1972 | 1.0703 | | 0.1983 | 1.72 | 9500 | 0.1961 | 1.0584 | | 0.1983 | 1.74 | 9600 | 0.2031 | 1.0938 | | 0.1983 | 1.75 | 9700 | 0.2019 | 1.0891 | | 0.1983 | 1.77 | 9800 | 0.2006 | 1.0542 | | 0.1983 | 1.79 | 9900 | 0.1925 | 1.0627 | | 0.1961 | 1.81 | 10000 | 0.1976 | 1.0751 | | 0.1961 | 1.83 | 10100 | 0.2051 | 1.0611 | | 0.1961 | 1.85 | 10200 | 0.2037 | 1.0656 | | 0.1961 | 1.86 | 10300 | 0.2025 | 1.0291 | | 0.1961 | 1.88 | 10400 | 0.1977 | 1.0525 | | 0.2025 | 1.9 | 10500 | 0.2030 | 1.0670 | | 0.2025 | 1.92 | 10600 | 0.1980 | 1.0765 | | 0.2025 | 1.94 | 10700 | 0.1975 | 1.0254 | | 0.2025 | 1.95 | 10800 | 0.1986 | 1.0636 | | 0.2025 | 1.97 | 10900 | 0.1956 | 1.0352 | | 0.2025 | 1.99 | 11000 | 0.1954 | 1.0265 | | 0.2025 | 2.01 | 11100 | 0.1957 | 1.0752 | | 0.2025 | 2.03 | 11200 | 0.1943 | 1.0784 | | 0.2025 | 2.04 | 11300 | 0.1898 | 1.0341 | | 0.2025 | 2.06 | 11400 | 0.1921 | 1.0301 | | 0.1805 | 2.08 | 11500 | 0.1910 | 1.0230 | | 0.1805 | 2.1 | 11600 | 0.1961 | 1.0203 | | 0.1805 | 2.12 | 11700 | 0.1973 | 1.0776 | | 0.1805 | 2.13 | 11800 | 0.1876 | 1.0788 | | 0.1805 | 2.15 | 11900 | 0.1934 | 1.0251 | | 0.177 | 2.17 | 12000 | 0.1967 | 1.0340 | | 0.177 | 2.19 | 12100 | 0.1932 | 1.0131 | | 0.177 | 2.21 | 12200 | 0.1926 | 1.0078 | | 0.177 | 2.23 | 12300 | 0.1947 | 0.9991 | | 0.177 | 2.24 | 12400 | 0.1914 | 1.0213 | | 0.1782 | 2.26 | 12500 | 0.1962 | 0.9882 | | 0.1782 | 2.28 | 12600 | 0.1960 | 1.0562 | | 0.1782 | 2.3 | 12700 | 0.2006 | 1.0401 | | 0.1782 | 2.32 | 12800 | 0.1950 | 1.0688 | | 0.1782 | 2.33 | 12900 | 0.1920 | 1.0435 | | 0.1796 | 2.35 | 13000 | 0.1926 | 1.0667 | | 0.1796 | 2.37 | 13100 | 0.1949 | 1.0859 | | 0.1796 | 2.39 | 13200 | 0.1932 | 1.0670 | | 0.1796 | 2.41 | 13300 | 0.1882 | 1.0663 | | 0.1796 | 2.42 | 13400 | 0.1877 | 1.0760 | | 0.1775 | 2.44 | 13500 | 0.1893 | 1.0859 | | 0.1775 | 2.46 | 13600 | 0.1936 | 1.0702 | | 0.1775 | 2.48 | 13700 | 0.1871 | 1.0414 | | 0.1775 | 2.5 | 13800 | 0.1917 | 1.0430 | | 0.1775 | 2.51 | 13900 | 0.1922 | 1.0422 | | 0.1778 | 2.53 | 14000 | 0.1875 | 1.0585 | | 0.1778 | 2.55 | 14100 | 0.1876 | 1.0603 | | 0.1778 | 2.57 | 14200 | 0.1888 | 1.0628 | | 0.1778 | 2.59 | 14300 | 0.1948 | 1.0782 | | 0.1778 | 2.6 | 14400 | 0.1942 | 1.0695 | | 0.1784 | 2.62 | 14500 | 0.1842 | 1.0863 | | 0.1784 | 2.64 | 14600 | 0.1850 | 1.0543 | | 0.1784 | 2.66 | 14700 | 0.1824 | 1.0683 | | 0.1784 | 2.68 | 14800 | 0.1888 | 1.0693 | | 0.1784 | 2.7 | 14900 | 0.1871 | 1.0175 | | 0.1753 | 2.71 | 15000 | 0.1889 | 1.0549 | | 0.1753 | 2.73 | 15100 | 0.1865 | 1.0544 | | 0.1753 | 2.75 | 15200 | 0.1918 | 1.0726 | | 0.1753 | 2.77 | 15300 | 0.1964 | 1.0915 | | 0.1753 | 2.79 | 15400 | 0.1900 | 1.0610 | | 0.1768 | 2.8 | 15500 | 0.1894 | 1.0763 | | 0.1768 | 2.82 | 15600 | 0.1882 | 1.0548 | | 0.1768 | 2.84 | 15700 | 0.1861 | 1.0902 | | 0.1768 | 2.86 | 15800 | 0.1860 | 1.0551 | | 0.1768 | 2.88 | 15900 | 0.1879 | 1.0581 | | 0.1761 | 2.89 | 16000 | 0.1899 | 1.0544 | | 0.1761 | 2.91 | 16100 | 0.1860 | 1.0530 | | 0.1761 | 2.93 | 16200 | 0.1894 | 1.0596 | | 0.1761 | 2.95 | 16300 | 0.1835 | 1.0394 | | 0.1761 | 2.97 | 16400 | 0.1852 | 1.0445 | | 0.1754 | 2.98 | 16500 | 0.1847 | 1.0390 | | 0.1754 | 3.0 | 16600 | 0.1828 | 1.0440 | | 0.1754 | 3.02 | 16700 | 0.1869 | 1.0560 | | 0.1754 | 3.04 | 16800 | 0.1882 | 1.0573 | | 0.1754 | 3.06 | 16900 | 0.1912 | 1.0600 | | 0.1592 | 3.08 | 17000 | 0.1921 | 1.0529 | | 0.1592 | 3.09 | 17100 | 0.1881 | 1.0175 | | 0.1592 | 3.11 | 17200 | 0.1891 | 1.0654 | | 0.1592 | 3.13 | 17300 | 0.1889 | 1.0687 | | 0.1592 | 3.15 | 17400 | 0.1916 | 1.0642 | | 0.1556 | 3.17 | 17500 | 0.1850 | 1.0295 | | 0.1556 | 3.18 | 17600 | 0.1875 | 1.0273 | | 0.1556 | 3.2 | 17700 | 0.1894 | 1.0051 | | 0.1556 | 3.22 | 17800 | 0.1870 | 1.0462 | | 0.1556 | 3.24 | 17900 | 0.1831 | 1.0308 | | 0.1557 | 3.26 | 18000 | 0.1878 | 1.0603 | | 0.1557 | 3.27 | 18100 | 0.1850 | 1.0566 | | 0.1557 | 3.29 | 18200 | 0.1843 | 1.0629 | | 0.1557 | 3.31 | 18300 | 0.1886 | 1.0378 | | 0.1557 | 3.33 | 18400 | 0.1892 | 1.0381 | | 0.159 | 3.35 | 18500 | 0.1942 | 1.0519 | | 0.159 | 3.36 | 18600 | 0.1829 | 1.0622 | | 0.159 | 3.38 | 18700 | 0.1894 | 1.0557 | | 0.159 | 3.4 | 18800 | 0.1895 | 1.0627 | | 0.159 | 3.42 | 18900 | 0.1863 | 1.0362 | | 0.1582 | 3.44 | 19000 | 0.1888 | 1.0491 | | 0.1582 | 3.46 | 19100 | 0.1854 | 1.0483 | | 0.1582 | 3.47 | 19200 | 0.1797 | 0.9787 | | 0.1582 | 3.49 | 19300 | 0.1785 | 1.0086 | | 0.1582 | 3.51 | 19400 | 0.1797 | 0.9915 | | 0.1507 | 3.53 | 19500 | 0.1873 | 1.0266 | | 0.1507 | 3.55 | 19600 | 0.1838 | 1.0299 | | 0.1507 | 3.56 | 19700 | 0.1817 | 1.0355 | | 0.1507 | 3.58 | 19800 | 0.1819 | 1.0271 | | 0.1507 | 3.6 | 19900 | 0.1883 | 1.0248 | | 0.1601 | 3.62 | 20000 | 0.1823 | 1.0406 | | 0.1601 | 3.64 | 20100 | 0.1801 | 1.0261 | | 0.1601 | 3.65 | 20200 | 0.1783 | 1.0329 | | 0.1601 | 3.67 | 20300 | 0.1857 | 1.0162 | | 0.1601 | 3.69 | 20400 | 0.1814 | 1.0212 | | 0.1552 | 3.71 | 20500 | 0.1837 | 1.0232 | | 0.1552 | 3.73 | 20600 | 0.1843 | 1.0314 | | 0.1552 | 3.74 | 20700 | 0.1842 | 1.0258 | | 0.1552 | 3.76 | 20800 | 0.1821 | 1.0479 | | 0.1552 | 3.78 | 20900 | 0.1864 | 1.0459 | | 0.1576 | 3.8 | 21000 | 0.1831 | 1.0364 | | 0.1576 | 3.82 | 21100 | 0.1852 | 1.0271 | | 0.1576 | 3.83 | 21200 | 0.1865 | 1.0204 | | 0.1576 | 3.85 | 21300 | 0.1794 | 1.0324 | | 0.1576 | 3.87 | 21400 | 0.1826 | 1.0315 | | 0.1585 | 3.89 | 21500 | 0.1824 | 1.0327 | | 0.1585 | 3.91 | 21600 | 0.1838 | 1.0208 | | 0.1585 | 3.93 | 21700 | 0.1850 | 1.0199 | | 0.1585 | 3.94 | 21800 | 0.1841 | 1.0050 | | 0.1585 | 3.96 | 21900 | 0.1783 | 1.0003 | | 0.1572 | 3.98 | 22000 | 0.1787 | 1.0115 | | 0.1572 | 4.0 | 22100 | 0.1810 | 1.0235 | | 0.1572 | 4.02 | 22200 | 0.1763 | 1.0191 | | 0.1572 | 4.03 | 22300 | 0.1764 | 1.0332 | | 0.1572 | 4.05 | 22400 | 0.1794 | 1.0429 | | 0.1406 | 4.07 | 22500 | 0.1905 | 1.0288 | | 0.1406 | 4.09 | 22600 | 0.1776 | 1.0244 | | 0.1406 | 4.11 | 22700 | 0.1782 | 1.0451 | | 0.1406 | 4.12 | 22800 | 0.1771 | 1.0387 | | 0.1406 | 4.14 | 22900 | 0.1788 | 1.0435 | | 0.14 | 4.16 | 23000 | 0.1792 | 1.0421 | | 0.14 | 4.18 | 23100 | 0.1841 | 1.0241 | | 0.14 | 4.2 | 23200 | 0.1769 | 1.0546 | | 0.14 | 4.21 | 23300 | 0.1815 | 1.0602 | | 0.14 | 4.23 | 23400 | 0.1784 | 1.0369 | | 0.1394 | 4.25 | 23500 | 0.1809 | 1.0406 | | 0.1394 | 4.27 | 23600 | 0.1744 | 1.0133 | | 0.1394 | 4.29 | 23700 | 0.1771 | 1.0214 | | 0.1394 | 4.31 | 23800 | 0.1765 | 1.0064 | | 0.1394 | 4.32 | 23900 | 0.1793 | 1.0200 | | 0.14 | 4.34 | 24000 | 0.1776 | 1.0352 | | 0.14 | 4.36 | 24100 | 0.1775 | 1.0294 | | 0.14 | 4.38 | 24200 | 0.1763 | 1.0213 | | 0.14 | 4.4 | 24300 | 0.1697 | 1.0302 | | 0.14 | 4.41 | 24400 | 0.1771 | 1.0259 | | 0.1408 | 4.43 | 24500 | 0.1747 | 1.0409 | | 0.1408 | 4.45 | 24600 | 0.1769 | 1.0278 | | 0.1408 | 4.47 | 24700 | 0.1767 | 1.0190 | | 0.1408 | 4.49 | 24800 | 0.1745 | 1.0281 | | 0.1408 | 4.5 | 24900 | 0.1738 | 1.0356 | | 0.1391 | 4.52 | 25000 | 0.1781 | 1.0429 | | 0.1391 | 4.54 | 25100 | 0.1784 | 1.0076 | | 0.1391 | 4.56 | 25200 | 0.1771 | 1.0157 | | 0.1391 | 4.58 | 25300 | 0.1758 | 1.0337 | | 0.1391 | 4.59 | 25400 | 0.1758 | 1.0466 | | 0.1398 | 4.61 | 25500 | 0.1724 | 1.0403 | | 0.1398 | 4.63 | 25600 | 0.1765 | 1.0481 | | 0.1398 | 4.65 | 25700 | 0.1757 | 1.0320 | | 0.1398 | 4.67 | 25800 | 0.1814 | 1.0479 | | 0.1398 | 4.69 | 25900 | 0.1713 | 1.0251 | | 0.1427 | 4.7 | 26000 | 0.1735 | 1.0340 | | 0.1427 | 4.72 | 26100 | 0.1765 | 1.0358 | | 0.1427 | 4.74 | 26200 | 0.1731 | 1.0220 | | 0.1427 | 4.76 | 26300 | 0.1769 | 1.0261 | | 0.1427 | 4.78 | 26400 | 0.1747 | 1.0139 | | 0.1424 | 4.79 | 26500 | 0.1791 | 1.0406 | | 0.1424 | 4.81 | 26600 | 0.1735 | 1.0497 | | 0.1424 | 4.83 | 26700 | 0.1710 | 1.0433 | | 0.1424 | 4.85 | 26800 | 0.1771 | 1.0002 | | 0.1424 | 4.87 | 26900 | 0.1748 | 1.0046 | | 0.1419 | 4.88 | 27000 | 0.1794 | 1.0332 | | 0.1419 | 4.9 | 27100 | 0.1772 | 1.0558 | | 0.1419 | 4.92 | 27200 | 0.1757 | 1.0477 | | 0.1419 | 4.94 | 27300 | 0.1735 | 1.0324 | | 0.1419 | 4.96 | 27400 | 0.1758 | 1.0260 | | 0.1433 | 4.97 | 27500 | 0.1767 | 1.0422 | | 0.1433 | 4.99 | 27600 | 0.1695 | 1.0386 | | 0.1433 | 5.01 | 27700 | 0.1763 | 1.0571 | | 0.1433 | 5.03 | 27800 | 0.1743 | 1.0367 | | 0.1433 | 5.05 | 27900 | 0.1804 | 1.0255 | | 0.1306 | 5.07 | 28000 | 0.1803 | 1.0377 | | 0.1306 | 5.08 | 28100 | 0.1750 | 1.0552 | | 0.1306 | 5.1 | 28200 | 0.1743 | 1.0512 | | 0.1306 | 5.12 | 28300 | 0.1777 | 1.0584 | | 0.1306 | 5.14 | 28400 | 0.1726 | 1.0374 | | 0.123 | 5.16 | 28500 | 0.1776 | 1.0439 | | 0.123 | 5.17 | 28600 | 0.1759 | 1.0682 | | 0.123 | 5.19 | 28700 | 0.1724 | 1.0511 | | 0.123 | 5.21 | 28800 | 0.1677 | 1.0560 | | 0.123 | 5.23 | 28900 | 0.1699 | 1.0421 | | 0.1217 | 5.25 | 29000 | 0.1803 | 1.0370 | | 0.1217 | 5.26 | 29100 | 0.1770 | 1.0474 | | 0.1217 | 5.28 | 29200 | 0.1733 | 1.0332 | | 0.1217 | 5.3 | 29300 | 0.1746 | 1.0158 | | 0.1217 | 5.32 | 29400 | 0.1763 | 1.0341 | | 0.1246 | 5.34 | 29500 | 0.1775 | 1.0348 | | 0.1246 | 5.35 | 29600 | 0.1730 | 1.0492 | | 0.1246 | 5.37 | 29700 | 0.1730 | 1.0503 | | 0.1246 | 5.39 | 29800 | 0.1727 | 1.0437 | | 0.1246 | 5.41 | 29900 | 0.1744 | 1.0539 | | 0.127 | 5.43 | 30000 | 0.1748 | 1.0463 | | 0.127 | 5.44 | 30100 | 0.1746 | 1.0555 | | 0.127 | 5.46 | 30200 | 0.1810 | 1.0558 | | 0.127 | 5.48 | 30300 | 0.1773 | 1.0407 | | 0.127 | 5.5 | 30400 | 0.1722 | 1.0489 | | 0.1276 | 5.52 | 30500 | 0.1720 | 1.0520 | | 0.1276 | 5.54 | 30600 | 0.1777 | 1.0347 | | 0.1276 | 5.55 | 30700 | 0.1685 | 1.0347 | | 0.1276 | 5.57 | 30800 | 0.1659 | 1.0338 | | 0.1276 | 5.59 | 30900 | 0.1756 | 1.0228 | | 0.1246 | 5.61 | 31000 | 0.1717 | 1.0409 | | 0.1246 | 5.63 | 31100 | 0.1764 | 1.0202 | | 0.1246 | 5.64 | 31200 | 0.1693 | 1.0314 | | 0.1246 | 5.66 | 31300 | 0.1731 | 1.0319 | | 0.1246 | 5.68 | 31400 | 0.1688 | 1.0380 | | 0.1271 | 5.7 | 31500 | 0.1671 | 1.0350 | | 0.1271 | 5.72 | 31600 | 0.1676 | 1.0430 | | 0.1271 | 5.73 | 31700 | 0.1656 | 1.0441 | | 0.1271 | 5.75 | 31800 | 0.1664 | 1.0403 | | 0.1271 | 5.77 | 31900 | 0.1691 | 1.0152 | | 0.1259 | 5.79 | 32000 | 0.1702 | 1.0018 | | 0.1259 | 5.81 | 32100 | 0.1664 | 1.0246 | | 0.1259 | 5.82 | 32200 | 0.1737 | 1.0340 | | 0.1259 | 5.84 | 32300 | 0.1742 | 1.0449 | | 0.1259 | 5.86 | 32400 | 0.1707 | 1.0279 | | 0.1273 | 5.88 | 32500 | 0.1697 | 1.0471 | | 0.1273 | 5.9 | 32600 | 0.1668 | 1.0322 | | 0.1273 | 5.92 | 32700 | 0.1706 | 1.0378 | | 0.1273 | 5.93 | 32800 | 0.1704 | 1.0350 | | 0.1273 | 5.95 | 32900 | 0.1725 | 1.0244 | | 0.123 | 5.97 | 33000 | 0.1678 | 1.0447 | | 0.123 | 5.99 | 33100 | 0.1681 | 1.0438 | | 0.123 | 6.01 | 33200 | 0.1689 | 1.0297 | | 0.123 | 6.02 | 33300 | 0.1690 | 1.0333 | | 0.123 | 6.04 | 33400 | 0.1734 | 1.0296 | | 0.1163 | 6.06 | 33500 | 0.1748 | 1.0307 | | 0.1163 | 6.08 | 33600 | 0.1715 | 1.0123 | | 0.1163 | 6.1 | 33700 | 0.1668 | 1.0117 | | 0.1163 | 6.11 | 33800 | 0.1690 | 1.0230 | | 0.1163 | 6.13 | 33900 | 0.1693 | 1.0166 | | 0.1101 | 6.15 | 34000 | 0.1728 | 1.0162 | | 0.1101 | 6.17 | 34100 | 0.1683 | 1.0107 | | 0.1101 | 6.19 | 34200 | 0.1703 | 0.9814 | | 0.1101 | 6.2 | 34300 | 0.1692 | 1.0007 | | 0.1101 | 6.22 | 34400 | 0.1690 | 1.0000 | | 0.1118 | 6.24 | 34500 | 0.1734 | 0.9972 | | 0.1118 | 6.26 | 34600 | 0.1739 | 1.0096 | | 0.1118 | 6.28 | 34700 | 0.1749 | 1.0047 | | 0.1118 | 6.3 | 34800 | 0.1709 | 1.0111 | | 0.1118 | 6.31 | 34900 | 0.1717 | 1.0179 | | 0.1153 | 6.33 | 35000 | 0.1690 | 1.0155 | | 0.1153 | 6.35 | 35100 | 0.1710 | 1.0144 | | 0.1153 | 6.37 | 35200 | 0.1719 | 1.0030 | | 0.1153 | 6.39 | 35300 | 0.1690 | 1.0272 | | 0.1153 | 6.4 | 35400 | 0.1673 | 1.0103 | | 0.1106 | 6.42 | 35500 | 0.1710 | 1.0222 | | 0.1106 | 6.44 | 35600 | 0.1747 | 1.0173 | | 0.1106 | 6.46 | 35700 | 0.1721 | 0.9933 | | 0.1106 | 6.48 | 35800 | 0.1670 | 1.0184 | | 0.1106 | 6.49 | 35900 | 0.1714 | 1.0122 | | 0.1116 | 6.51 | 36000 | 0.1717 | 1.0035 | | 0.1116 | 6.53 | 36100 | 0.1685 | 1.0099 | | 0.1116 | 6.55 | 36200 | 0.1687 | 1.0288 | | 0.1116 | 6.57 | 36300 | 0.1664 | 1.0314 | | 0.1116 | 6.58 | 36400 | 0.1665 | 1.0264 | | 0.1128 | 6.6 | 36500 | 0.1681 | 1.0420 | | 0.1128 | 6.62 | 36600 | 0.1682 | 1.0409 | | 0.1128 | 6.64 | 36700 | 0.1717 | 1.0271 | | 0.1128 | 6.66 | 36800 | 0.1717 | 1.0166 | | 0.1128 | 6.68 | 36900 | 0.1755 | 1.0175 | | 0.1134 | 6.69 | 37000 | 0.1623 | 1.0185 | | 0.1134 | 6.71 | 37100 | 0.1674 | 1.0302 | | 0.1134 | 6.73 | 37200 | 0.1633 | 1.0325 | | 0.1134 | 6.75 | 37300 | 0.1628 | 1.0228 | | 0.1134 | 6.77 | 37400 | 0.1636 | 1.0243 | | 0.1102 | 6.78 | 37500 | 0.1667 | 1.0282 | | 0.1102 | 6.8 | 37600 | 0.1623 | 1.0212 | | 0.1102 | 6.82 | 37700 | 0.1639 | 1.0140 | | 0.1102 | 6.84 | 37800 | 0.1587 | 1.0258 | | 0.1102 | 6.86 | 37900 | 0.1610 | 1.0087 | | 0.1113 | 6.87 | 38000 | 0.1647 | 1.0199 | | 0.1113 | 6.89 | 38100 | 0.1609 | 1.0054 | | 0.1113 | 6.91 | 38200 | 0.1602 | 1.0145 | | 0.1113 | 6.93 | 38300 | 0.1602 | 1.0144 | | 0.1113 | 6.95 | 38400 | 0.1602 | 1.0375 | | 0.1071 | 6.96 | 38500 | 0.1592 | 1.0259 | | 0.1071 | 6.98 | 38600 | 0.1612 | 1.0236 | | 0.1071 | 7.0 | 38700 | 0.1621 | 1.0277 | | 0.1071 | 7.02 | 38800 | 0.1669 | 1.0367 | | 0.1071 | 7.04 | 38900 | 0.1742 | 1.0484 | | 0.1062 | 7.05 | 39000 | 0.1752 | 1.0302 | | 0.1062 | 7.07 | 39100 | 0.1676 | 1.0244 | | 0.1062 | 7.09 | 39200 | 0.1723 | 1.0300 | | 0.1062 | 7.11 | 39300 | 0.1727 | 1.0294 | | 0.1062 | 7.13 | 39400 | 0.1711 | 1.0255 | | 0.1021 | 7.15 | 39500 | 0.1699 | 1.0471 | | 0.1021 | 7.16 | 39600 | 0.1682 | 1.0426 | | 0.1021 | 7.18 | 39700 | 0.1713 | 1.0233 | | 0.1021 | 7.2 | 39800 | 0.1682 | 1.0259 | | 0.1021 | 7.22 | 39900 | 0.1710 | 1.0162 | | 0.103 | 7.24 | 40000 | 0.1725 | 1.0283 | | 0.103 | 7.25 | 40100 | 0.1729 | 1.0264 | | 0.103 | 7.27 | 40200 | 0.1665 | 1.0451 | | 0.103 | 7.29 | 40300 | 0.1671 | 1.0386 | | 0.103 | 7.31 | 40400 | 0.1671 | 1.0316 | | 0.0981 | 7.33 | 40500 | 0.1708 | 1.0257 | | 0.0981 | 7.34 | 40600 | 0.1642 | 1.0152 | | 0.0981 | 7.36 | 40700 | 0.1707 | 1.0110 | | 0.0981 | 7.38 | 40800 | 0.1675 | 1.0186 | | 0.0981 | 7.4 | 40900 | 0.1702 | 1.0123 | | 0.1005 | 7.42 | 41000 | 0.1699 | 1.0159 | | 0.1005 | 7.43 | 41100 | 0.1703 | 1.0219 | | 0.1005 | 7.45 | 41200 | 0.1707 | 1.0194 | | 0.1005 | 7.47 | 41300 | 0.1644 | 1.0016 | | 0.1005 | 7.49 | 41400 | 0.1716 | 0.9941 | | 0.1021 | 7.51 | 41500 | 0.1670 | 1.0159 | | 0.1021 | 7.53 | 41600 | 0.1667 | 1.0033 | | 0.1021 | 7.54 | 41700 | 0.1667 | 1.0176 | | 0.1021 | 7.56 | 41800 | 0.1679 | 1.0194 | | 0.1021 | 7.58 | 41900 | 0.1632 | 1.0418 | | 0.0963 | 7.6 | 42000 | 0.1712 | 1.0152 | | 0.0963 | 7.62 | 42100 | 0.1632 | 1.0364 | | 0.0963 | 7.63 | 42200 | 0.1702 | 1.0229 | | 0.0963 | 7.65 | 42300 | 0.1655 | 1.0179 | | 0.0963 | 7.67 | 42400 | 0.1698 | 1.0329 | | 0.1014 | 7.69 | 42500 | 0.1691 | 1.0398 | | 0.1014 | 7.71 | 42600 | 0.1638 | 1.0487 | | 0.1014 | 7.72 | 42700 | 0.1617 | 1.0210 | | 0.1014 | 7.74 | 42800 | 0.1648 | 1.0124 | | 0.1014 | 7.76 | 42900 | 0.1608 | 1.0202 | | 0.1008 | 7.78 | 43000 | 0.1611 | 1.0353 | | 0.1008 | 7.8 | 43100 | 0.1633 | 1.0319 | | 0.1008 | 7.81 | 43200 | 0.1640 | 1.0032 | | 0.1008 | 7.83 | 43300 | 0.1589 | 0.9985 | | 0.1008 | 7.85 | 43400 | 0.1630 | 0.9975 | | 0.0988 | 7.87 | 43500 | 0.1604 | 1.0053 | | 0.0988 | 7.89 | 43600 | 0.1687 | 1.0063 | | 0.0988 | 7.91 | 43700 | 0.1619 | 1.0096 | | 0.0988 | 7.92 | 43800 | 0.1565 | 0.9901 | | 0.0988 | 7.94 | 43900 | 0.1619 | 0.9742 | | 0.102 | 7.96 | 44000 | 0.1598 | 0.9593 | | 0.102 | 7.98 | 44100 | 0.1635 | 0.9718 | | 0.102 | 8.0 | 44200 | 0.1624 | 0.9903 | | 0.102 | 8.01 | 44300 | 0.1605 | 0.9882 | | 0.102 | 8.03 | 44400 | 0.1657 | 1.0128 | | 0.0961 | 8.05 | 44500 | 0.1651 | 1.0155 | | 0.0961 | 8.07 | 44600 | 0.1680 | 1.0194 | | 0.0961 | 8.09 | 44700 | 0.1694 | 1.0112 | | 0.0961 | 8.1 | 44800 | 0.1665 | 1.0073 | | 0.0961 | 8.12 | 44900 | 0.1612 | 1.0200 | | 0.0894 | 8.14 | 45000 | 0.1652 | 1.0337 | | 0.0894 | 8.16 | 45100 | 0.1626 | 1.0086 | | 0.0894 | 8.18 | 45200 | 0.1639 | 1.0083 | | 0.0894 | 8.19 | 45300 | 0.1634 | 1.0223 | | 0.0894 | 8.21 | 45400 | 0.1631 | 1.0339 | | 0.0887 | 8.23 | 45500 | 0.1640 | 1.0311 | | 0.0887 | 8.25 | 45600 | 0.1661 | 1.0264 | | 0.0887 | 8.27 | 45700 | 0.1650 | 1.0315 | | 0.0887 | 8.29 | 45800 | 0.1624 | 1.0390 | | 0.0887 | 8.3 | 45900 | 0.1624 | 1.0350 | | 0.0884 | 8.32 | 46000 | 0.1615 | 1.0318 | | 0.0884 | 8.34 | 46100 | 0.1628 | 1.0410 | | 0.0884 | 8.36 | 46200 | 0.1627 | 1.0429 | | 0.0884 | 8.38 | 46300 | 0.1644 | 1.0320 | | 0.0884 | 8.39 | 46400 | 0.1633 | 1.0177 | | 0.0893 | 8.41 | 46500 | 0.1654 | 1.0189 | | 0.0893 | 8.43 | 46600 | 0.1598 | 1.0154 | | 0.0893 | 8.45 | 46700 | 0.1618 | 1.0250 | | 0.0893 | 8.47 | 46800 | 0.1639 | 1.0402 | | 0.0893 | 8.48 | 46900 | 0.1616 | 1.0336 | | 0.0869 | 8.5 | 47000 | 0.1613 | 1.0296 | | 0.0869 | 8.52 | 47100 | 0.1648 | 1.0568 | | 0.0869 | 8.54 | 47200 | 0.1625 | 1.0256 | | 0.0869 | 8.56 | 47300 | 0.1609 | 1.0390 | | 0.0869 | 8.57 | 47400 | 0.1606 | 1.0450 | | 0.0894 | 8.59 | 47500 | 0.1605 | 1.0445 | | 0.0894 | 8.61 | 47600 | 0.1660 | 1.0402 | | 0.0894 | 8.63 | 47700 | 0.1618 | 1.0444 | | 0.0894 | 8.65 | 47800 | 0.1669 | 1.0333 | | 0.0894 | 8.66 | 47900 | 0.1627 | 1.0364 | | 0.0885 | 8.68 | 48000 | 0.1616 | 1.0334 | | 0.0885 | 8.7 | 48100 | 0.1626 | 1.0564 | | 0.0885 | 8.72 | 48200 | 0.1624 | 1.0396 | | 0.0885 | 8.74 | 48300 | 0.1623 | 1.0396 | | 0.0885 | 8.76 | 48400 | 0.1612 | 1.0112 | | 0.0888 | 8.77 | 48500 | 0.1638 | 1.0292 | | 0.0888 | 8.79 | 48600 | 0.1639 | 0.9988 | | 0.0888 | 8.81 | 48700 | 0.1618 | 1.0127 | | 0.0888 | 8.83 | 48800 | 0.1584 | 1.0042 | | 0.0888 | 8.85 | 48900 | 0.1615 | 1.0041 | | 0.0887 | 8.86 | 49000 | 0.1637 | 1.0269 | | 0.0887 | 8.88 | 49100 | 0.1627 | 0.9989 | | 0.0887 | 8.9 | 49200 | 0.1583 | 1.0104 | | 0.0887 | 8.92 | 49300 | 0.1600 | 1.0214 | | 0.0887 | 8.94 | 49400 | 0.1599 | 1.0126 | | 0.0893 | 8.95 | 49500 | 0.1595 | 1.0516 | | 0.0893 | 8.97 | 49600 | 0.1625 | 1.0464 | | 0.0893 | 8.99 | 49700 | 0.1595 | 1.0361 | | 0.0893 | 9.01 | 49800 | 0.1614 | 1.0469 | | 0.0893 | 9.03 | 49900 | 0.1612 | 1.0304 | | 0.0834 | 9.04 | 50000 | 0.1643 | 1.0335 | | 0.0834 | 9.06 | 50100 | 0.1640 | 1.0175 | | 0.0834 | 9.08 | 50200 | 0.1655 | 1.0264 | | 0.0834 | 9.1 | 50300 | 0.1678 | 1.0243 | | 0.0834 | 9.12 | 50400 | 0.1659 | 1.0145 | | 0.079 | 9.14 | 50500 | 0.1644 | 1.0316 | | 0.079 | 9.15 | 50600 | 0.1630 | 1.0326 | | 0.079 | 9.17 | 50700 | 0.1634 | 1.0154 | | 0.079 | 9.19 | 50800 | 0.1697 | 1.0095 | | 0.079 | 9.21 | 50900 | 0.1678 | 1.0050 | | 0.078 | 9.23 | 51000 | 0.1626 | 1.0159 | | 0.078 | 9.24 | 51100 | 0.1666 | 1.0238 | | 0.078 | 9.26 | 51200 | 0.1644 | 1.0244 | | 0.078 | 9.28 | 51300 | 0.1655 | 1.0345 | | 0.078 | 9.3 | 51400 | 0.1615 | 1.0237 | | 0.0776 | 9.32 | 51500 | 0.1664 | 1.0180 | | 0.0776 | 9.33 | 51600 | 0.1603 | 1.0208 | | 0.0776 | 9.35 | 51700 | 0.1594 | 1.0230 | | 0.0776 | 9.37 | 51800 | 0.1622 | 1.0201 | | 0.0776 | 9.39 | 51900 | 0.1596 | 1.0039 | | 0.0782 | 9.41 | 52000 | 0.1645 | 1.0204 | | 0.0782 | 9.42 | 52100 | 0.1640 | 1.0318 | | 0.0782 | 9.44 | 52200 | 0.1621 | 1.0290 | | 0.0782 | 9.46 | 52300 | 0.1638 | 1.0318 | | 0.0782 | 9.48 | 52400 | 0.1613 | 1.0217 | | 0.0782 | 9.5 | 52500 | 0.1609 | 1.0261 | | 0.0782 | 9.52 | 52600 | 0.1625 | 1.0101 | | 0.0782 | 9.53 | 52700 | 0.1613 | 1.0058 | | 0.0782 | 9.55 | 52800 | 0.1599 | 1.0068 | | 0.0782 | 9.57 | 52900 | 0.1600 | 1.0110 | | 0.0797 | 9.59 | 53000 | 0.1594 | 1.0171 | | 0.0797 | 9.61 | 53100 | 0.1583 | 1.0124 | | 0.0797 | 9.62 | 53200 | 0.1646 | 1.0093 | | 0.0797 | 9.64 | 53300 | 0.1580 | 1.0201 | | 0.0797 | 9.66 | 53400 | 0.1599 | 1.0207 | | 0.0783 | 9.68 | 53500 | 0.1577 | 1.0226 | | 0.0783 | 9.7 | 53600 | 0.1593 | 1.0160 | | 0.0783 | 9.71 | 53700 | 0.1570 | 1.0173 | | 0.0783 | 9.73 | 53800 | 0.1614 | 1.0299 | | 0.0783 | 9.75 | 53900 | 0.1610 | 1.0184 | | 0.0779 | 9.77 | 54000 | 0.1606 | 1.0173 | | 0.0779 | 9.79 | 54100 | 0.1577 | 1.0032 | | 0.0779 | 9.8 | 54200 | 0.1590 | 1.0070 | | 0.0779 | 9.82 | 54300 | 0.1580 | 1.0257 | | 0.0779 | 9.84 | 54400 | 0.1592 | 1.0108 | | 0.0778 | 9.86 | 54500 | 0.1617 | 0.9907 | | 0.0778 | 9.88 | 54600 | 0.1605 | 1.0189 | | 0.0778 | 9.89 | 54700 | 0.1605 | 1.0177 | | 0.0778 | 9.91 | 54800 | 0.1536 | 1.0275 | | 0.0778 | 9.93 | 54900 | 0.1658 | 1.0282 | | 0.0777 | 9.95 | 55000 | 0.1543 | 1.0385 | | 0.0777 | 9.97 | 55100 | 0.1559 | 1.0375 | | 0.0777 | 9.99 | 55200 | 0.1590 | 1.0215 | | 0.0777 | 10.0 | 55300 | 0.1624 | 1.0242 | | 0.0777 | 10.02 | 55400 | 0.1635 | 1.0244 | | 0.0712 | 10.04 | 55500 | 0.1629 | 1.0298 | | 0.0712 | 10.06 | 55600 | 0.1601 | 1.0299 | | 0.0712 | 10.08 | 55700 | 0.1625 | 1.0117 | | 0.0712 | 10.09 | 55800 | 0.1650 | 1.0233 | | 0.0712 | 10.11 | 55900 | 0.1631 | 1.0061 | | 0.0667 | 10.13 | 56000 | 0.1637 | 1.0226 | | 0.0667 | 10.15 | 56100 | 0.1607 | 1.0042 | | 0.0667 | 10.17 | 56200 | 0.1599 | 1.0117 | | 0.0667 | 10.18 | 56300 | 0.1623 | 1.0246 | | 0.0667 | 10.2 | 56400 | 0.1639 | 1.0294 | | 0.0695 | 10.22 | 56500 | 0.1650 | 1.0232 | | 0.0695 | 10.24 | 56600 | 0.1620 | 1.0289 | | 0.0695 | 10.26 | 56700 | 0.1667 | 1.0209 | | 0.0695 | 10.27 | 56800 | 0.1580 | 1.0163 | | 0.0695 | 10.29 | 56900 | 0.1646 | 1.0293 | | 0.0686 | 10.31 | 57000 | 0.1636 | 1.0106 | | 0.0686 | 10.33 | 57100 | 0.1586 | 1.0044 | | 0.0686 | 10.35 | 57200 | 0.1582 | 1.0213 | | 0.0686 | 10.37 | 57300 | 0.1627 | 1.0151 | | 0.0686 | 10.38 | 57400 | 0.1619 | 1.0248 | | 0.0686 | 10.4 | 57500 | 0.1596 | 1.0098 | | 0.0686 | 10.42 | 57600 | 0.1606 | 1.0031 | | 0.0686 | 10.44 | 57700 | 0.1620 | 1.0046 | | 0.0686 | 10.46 | 57800 | 0.1592 | 1.0018 | | 0.0686 | 10.47 | 57900 | 0.1592 | 1.0058 | | 0.0669 | 10.49 | 58000 | 0.1605 | 0.9961 | | 0.0669 | 10.51 | 58100 | 0.1632 | 1.0102 | | 0.0669 | 10.53 | 58200 | 0.1593 | 1.0061 | | 0.0669 | 10.55 | 58300 | 0.1586 | 1.0091 | | 0.0669 | 10.56 | 58400 | 0.1603 | 1.0085 | | 0.068 | 10.58 | 58500 | 0.1579 | 1.0031 | | 0.068 | 10.6 | 58600 | 0.1591 | 1.0021 | | 0.068 | 10.62 | 58700 | 0.1590 | 1.0163 | | 0.068 | 10.64 | 58800 | 0.1584 | 1.0045 | | 0.068 | 10.65 | 58900 | 0.1594 | 1.0158 | | 0.0693 | 10.67 | 59000 | 0.1568 | 1.0052 | | 0.0693 | 10.69 | 59100 | 0.1581 | 0.9955 | | 0.0693 | 10.71 | 59200 | 0.1622 | 0.9917 | | 0.0693 | 10.73 | 59300 | 0.1580 | 1.0018 | | 0.0693 | 10.75 | 59400 | 0.1601 | 1.0077 | | 0.0699 | 10.76 | 59500 | 0.1605 | 0.9997 | | 0.0699 | 10.78 | 59600 | 0.1585 | 1.0009 | | 0.0699 | 10.8 | 59700 | 0.1541 | 1.0058 | | 0.0699 | 10.82 | 59800 | 0.1583 | 1.0026 | | 0.0699 | 10.84 | 59900 | 0.1592 | 0.9992 | | 0.0671 | 10.85 | 60000 | 0.1590 | 1.0004 | | 0.0671 | 10.87 | 60100 | 0.1585 | 1.0060 | | 0.0671 | 10.89 | 60200 | 0.1579 | 1.0063 | | 0.0671 | 10.91 | 60300 | 0.1582 | 0.9949 | | 0.0671 | 10.93 | 60400 | 0.1562 | 1.0004 | | 0.0661 | 10.94 | 60500 | 0.1560 | 0.9950 | | 0.0661 | 10.96 | 60600 | 0.1564 | 0.9990 | | 0.0661 | 10.98 | 60700 | 0.1552 | 0.9982 | | 0.0661 | 11.0 | 60800 | 0.1596 | 1.0018 | | 0.0661 | 11.02 | 60900 | 0.1618 | 0.9905 | | 0.0634 | 11.03 | 61000 | 0.1652 | 0.9890 | | 0.0634 | 11.05 | 61100 | 0.1649 | 0.9886 | | 0.0634 | 11.07 | 61200 | 0.1668 | 0.9870 | | 0.0634 | 11.09 | 61300 | 0.1663 | 0.9921 | | 0.0634 | 11.11 | 61400 | 0.1650 | 0.9919 | | 0.0587 | 11.13 | 61500 | 0.1674 | 0.9831 | | 0.0587 | 11.14 | 61600 | 0.1633 | 0.9793 | | 0.0587 | 11.16 | 61700 | 0.1665 | 0.9781 | | 0.0587 | 11.18 | 61800 | 0.1642 | 0.9821 | | 0.0587 | 11.2 | 61900 | 0.1638 | 0.9797 | | 0.0581 | 11.22 | 62000 | 0.1628 | 0.9727 | | 0.0581 | 11.23 | 62100 | 0.1661 | 0.9796 | | 0.0581 | 11.25 | 62200 | 0.1641 | 0.9830 | | 0.0581 | 11.27 | 62300 | 0.1601 | 0.9867 | | 0.0581 | 11.29 | 62400 | 0.1626 | 0.9757 | | 0.0584 | 11.31 | 62500 | 0.1632 | 1.0014 | | 0.0584 | 11.32 | 62600 | 0.1626 | 1.0052 | | 0.0584 | 11.34 | 62700 | 0.1586 | 1.0098 | | 0.0584 | 11.36 | 62800 | 0.1597 | 1.0151 | | 0.0584 | 11.38 | 62900 | 0.1624 | 1.0054 | | 0.0589 | 11.4 | 63000 | 0.1618 | 1.0018 | | 0.0589 | 11.41 | 63100 | 0.1635 | 1.0032 | | 0.0589 | 11.43 | 63200 | 0.1654 | 1.0142 | | 0.0589 | 11.45 | 63300 | 0.1646 | 1.0031 | | 0.0589 | 11.47 | 63400 | 0.1618 | 1.0118 | | 0.0579 | 11.49 | 63500 | 0.1634 | 1.0218 | | 0.0579 | 11.51 | 63600 | 0.1616 | 1.0179 | | 0.0579 | 11.52 | 63700 | 0.1603 | 1.0036 | | 0.0579 | 11.54 | 63800 | 0.1610 | 1.0150 | | 0.0579 | 11.56 | 63900 | 0.1605 | 1.0285 | | 0.0572 | 11.58 | 64000 | 0.1621 | 1.0261 | | 0.0572 | 11.6 | 64100 | 0.1625 | 1.0252 | | 0.0572 | 11.61 | 64200 | 0.1677 | 1.0257 | | 0.0572 | 11.63 | 64300 | 0.1656 | 1.0243 | | 0.0572 | 11.65 | 64400 | 0.1669 | 1.0270 | | 0.0592 | 11.67 | 64500 | 0.1605 | 1.0305 | | 0.0592 | 11.69 | 64600 | 0.1633 | 1.0277 | | 0.0592 | 11.7 | 64700 | 0.1606 | 1.0176 | | 0.0592 | 11.72 | 64800 | 0.1618 | 1.0249 | | 0.0592 | 11.74 | 64900 | 0.1609 | 1.0113 | | 0.0595 | 11.76 | 65000 | 0.1609 | 1.0254 | | 0.0595 | 11.78 | 65100 | 0.1662 | 1.0275 | | 0.0595 | 11.79 | 65200 | 0.1652 | 1.0164 | | 0.0595 | 11.81 | 65300 | 0.1638 | 1.0266 | | 0.0595 | 11.83 | 65400 | 0.1589 | 1.0274 | | 0.0588 | 11.85 | 65500 | 0.1607 | 1.0136 | | 0.0588 | 11.87 | 65600 | 0.1592 | 1.0136 | | 0.0588 | 11.88 | 65700 | 0.1581 | 1.0183 | | 0.0588 | 11.9 | 65800 | 0.1587 | 1.0133 | | 0.0588 | 11.92 | 65900 | 0.1596 | 1.0170 | | 0.0558 | 11.94 | 66000 | 0.1590 | 1.0161 | | 0.0558 | 11.96 | 66100 | 0.1597 | 1.0193 | | 0.0558 | 11.98 | 66200 | 0.1590 | 1.0193 | | 0.0558 | 11.99 | 66300 | 0.1608 | 1.0242 | | 0.0558 | 12.01 | 66400 | 0.1642 | 1.0231 | | 0.0555 | 12.03 | 66500 | 0.1679 | 1.0168 | | 0.0555 | 12.05 | 66600 | 0.1674 | 1.0083 | | 0.0555 | 12.07 | 66700 | 0.1658 | 1.0069 | | 0.0555 | 12.08 | 66800 | 0.1661 | 1.0134 | | 0.0555 | 12.1 | 66900 | 0.1682 | 1.0274 | | 0.0508 | 12.12 | 67000 | 0.1702 | 1.0219 | | 0.0508 | 12.14 | 67100 | 0.1694 | 1.0219 | | 0.0508 | 12.16 | 67200 | 0.1667 | 1.0236 | | 0.0508 | 12.17 | 67300 | 0.1672 | 1.0253 | | 0.0508 | 12.19 | 67400 | 0.1640 | 1.0215 | | 0.0513 | 12.21 | 67500 | 0.1649 | 1.0242 | | 0.0513 | 12.23 | 67600 | 0.1687 | 1.0262 | | 0.0513 | 12.25 | 67700 | 0.1655 | 1.0231 | | 0.0513 | 12.26 | 67800 | 0.1692 | 1.0176 | | 0.0513 | 12.28 | 67900 | 0.1675 | 1.0202 | | 0.0519 | 12.3 | 68000 | 0.1644 | 1.0241 | | 0.0519 | 12.32 | 68100 | 0.1651 | 1.0297 | | 0.0519 | 12.34 | 68200 | 0.1661 | 1.0287 | | 0.0519 | 12.36 | 68300 | 0.1665 | 1.0257 | | 0.0519 | 12.37 | 68400 | 0.1685 | 1.0233 | | 0.0522 | 12.39 | 68500 | 0.1636 | 1.0177 | | 0.0522 | 12.41 | 68600 | 0.1709 | 1.0200 | | 0.0522 | 12.43 | 68700 | 0.1684 | 1.0164 | | 0.0522 | 12.45 | 68800 | 0.1666 | 1.0119 | | 0.0522 | 12.46 | 68900 | 0.1683 | 1.0136 | | 0.05 | 12.48 | 69000 | 0.1696 | 1.0127 | | 0.05 | 12.5 | 69100 | 0.1708 | 1.0184 | | 0.05 | 12.52 | 69200 | 0.1654 | 1.0282 | | 0.05 | 12.54 | 69300 | 0.1700 | 1.0235 | | 0.05 | 12.55 | 69400 | 0.1688 | 1.0257 | | 0.0513 | 12.57 | 69500 | 0.1646 | 1.0274 | | 0.0513 | 12.59 | 69600 | 0.1660 | 1.0247 | | 0.0513 | 12.61 | 69700 | 0.1657 | 1.0188 | | 0.0513 | 12.63 | 69800 | 0.1654 | 1.0087 | | 0.0513 | 12.64 | 69900 | 0.1681 | 1.0146 | | 0.0512 | 12.66 | 70000 | 0.1660 | 1.0185 | | 0.0512 | 12.68 | 70100 | 0.1690 | 1.0214 | | 0.0512 | 12.7 | 70200 | 0.1683 | 1.0160 | | 0.0512 | 12.72 | 70300 | 0.1695 | 1.0198 | | 0.0512 | 12.74 | 70400 | 0.1666 | 1.0193 | | 0.0484 | 12.75 | 70500 | 0.1654 | 1.0142 | | 0.0484 | 12.77 | 70600 | 0.1598 | 1.0154 | | 0.0484 | 12.79 | 70700 | 0.1623 | 1.0139 | | 0.0484 | 12.81 | 70800 | 0.1662 | 1.0180 | | 0.0484 | 12.83 | 70900 | 0.1659 | 1.0232 | | 0.0501 | 12.84 | 71000 | 0.1662 | 1.0202 | | 0.0501 | 12.86 | 71100 | 0.1639 | 1.0161 | | 0.0501 | 12.88 | 71200 | 0.1666 | 1.0151 | | 0.0501 | 12.9 | 71300 | 0.1644 | 1.0129 | | 0.0501 | 12.92 | 71400 | 0.1642 | 1.0171 | | 0.0482 | 12.93 | 71500 | 0.1635 | 1.0162 | | 0.0482 | 12.95 | 71600 | 0.1637 | 1.0186 | | 0.0482 | 12.97 | 71700 | 0.1639 | 1.0142 | | 0.0482 | 12.99 | 71800 | 0.1643 | 1.0122 | | 0.0482 | 13.01 | 71900 | 0.1679 | 1.0156 | | 0.0483 | 13.02 | 72000 | 0.1717 | 1.0224 | | 0.0483 | 13.04 | 72100 | 0.1742 | 1.0229 | | 0.0483 | 13.06 | 72200 | 0.1718 | 1.0237 | | 0.0483 | 13.08 | 72300 | 0.1742 | 1.0266 | | 0.0483 | 13.1 | 72400 | 0.1736 | 1.0257 | | 0.0443 | 13.12 | 72500 | 0.1741 | 1.0275 | | 0.0443 | 13.13 | 72600 | 0.1745 | 1.0325 | | 0.0443 | 13.15 | 72700 | 0.1737 | 1.0296 | | 0.0443 | 13.17 | 72800 | 0.1722 | 1.0303 | | 0.0443 | 13.19 | 72900 | 0.1702 | 1.0305 | | 0.0424 | 13.21 | 73000 | 0.1733 | 1.0241 | | 0.0424 | 13.22 | 73100 | 0.1748 | 1.0243 | | 0.0424 | 13.24 | 73200 | 0.1760 | 1.0231 | | 0.0424 | 13.26 | 73300 | 0.1745 | 1.0241 | | 0.0424 | 13.28 | 73400 | 0.1772 | 1.0217 | | 0.0424 | 13.3 | 73500 | 0.1755 | 1.0206 | | 0.0424 | 13.31 | 73600 | 0.1743 | 1.0242 | | 0.0424 | 13.33 | 73700 | 0.1738 | 1.0208 | | 0.0424 | 13.35 | 73800 | 0.1736 | 1.0249 | | 0.0424 | 13.37 | 73900 | 0.1747 | 1.0271 | | 0.0437 | 13.39 | 74000 | 0.1707 | 1.0241 | | 0.0437 | 13.4 | 74100 | 0.1731 | 1.0269 | | 0.0437 | 13.42 | 74200 | 0.1743 | 1.0290 | | 0.0437 | 13.44 | 74300 | 0.1739 | 1.0266 | | 0.0437 | 13.46 | 74400 | 0.1763 | 1.0246 | | 0.0443 | 13.48 | 74500 | 0.1724 | 1.0209 | | 0.0443 | 13.49 | 74600 | 0.1744 | 1.0244 | | 0.0443 | 13.51 | 74700 | 0.1717 | 1.0232 | | 0.0443 | 13.53 | 74800 | 0.1754 | 1.0217 | | 0.0443 | 13.55 | 74900 | 0.1721 | 1.0234 | | 0.0435 | 13.57 | 75000 | 0.1751 | 1.0197 | | 0.0435 | 13.59 | 75100 | 0.1727 | 1.0285 | | 0.0435 | 13.6 | 75200 | 0.1715 | 1.0221 | | 0.0435 | 13.62 | 75300 | 0.1746 | 1.0247 | | 0.0435 | 13.64 | 75400 | 0.1712 | 1.0231 | | 0.0436 | 13.66 | 75500 | 0.1719 | 1.0228 | | 0.0436 | 13.68 | 75600 | 0.1727 | 1.0197 | | 0.0436 | 13.69 | 75700 | 0.1750 | 1.0252 | | 0.0436 | 13.71 | 75800 | 0.1702 | 1.0241 | | 0.0436 | 13.73 | 75900 | 0.1720 | 1.0250 | | 0.0433 | 13.75 | 76000 | 0.1744 | 1.0210 | | 0.0433 | 13.77 | 76100 | 0.1735 | 1.0211 | | 0.0433 | 13.78 | 76200 | 0.1727 | 1.0205 | | 0.0433 | 13.8 | 76300 | 0.1706 | 1.0218 | | 0.0433 | 13.82 | 76400 | 0.1709 | 1.0238 | | 0.0431 | 13.84 | 76500 | 0.1705 | 1.0197 | | 0.0431 | 13.86 | 76600 | 0.1734 | 1.0223 | | 0.0431 | 13.87 | 76700 | 0.1695 | 1.0250 | | 0.0431 | 13.89 | 76800 | 0.1734 | 1.0232 | | 0.0431 | 13.91 | 76900 | 0.1724 | 1.0219 | | 0.041 | 13.93 | 77000 | 0.1706 | 1.0236 | | 0.041 | 13.95 | 77100 | 0.1689 | 1.0220 | | 0.041 | 13.97 | 77200 | 0.1738 | 1.0230 | | 0.041 | 13.98 | 77300 | 0.1727 | 1.0254 | | 0.041 | 14.0 | 77400 | 0.1721 | 1.0261 | | 0.041 | 14.02 | 77500 | 0.1760 | 1.0261 | | 0.041 | 14.04 | 77600 | 0.1772 | 1.0202 | | 0.041 | 14.06 | 77700 | 0.1782 | 1.0202 | | 0.041 | 14.07 | 77800 | 0.1777 | 1.0222 | | 0.041 | 14.09 | 77900 | 0.1787 | 1.0203 | | 0.0383 | 14.11 | 78000 | 0.1790 | 1.0236 | | 0.0383 | 14.13 | 78100 | 0.1812 | 1.0245 | | 0.0383 | 14.15 | 78200 | 0.1778 | 1.0224 | | 0.0383 | 14.16 | 78300 | 0.1771 | 1.0231 | | 0.0383 | 14.18 | 78400 | 0.1782 | 1.0242 | | 0.0391 | 14.2 | 78500 | 0.1785 | 1.0262 | | 0.0391 | 14.22 | 78600 | 0.1791 | 1.0261 | | 0.0391 | 14.24 | 78700 | 0.1770 | 1.0254 | | 0.0391 | 14.25 | 78800 | 0.1810 | 1.0257 | | 0.0391 | 14.27 | 78900 | 0.1794 | 1.0241 | | 0.0387 | 14.29 | 79000 | 0.1774 | 1.0256 | | 0.0387 | 14.31 | 79100 | 0.1774 | 1.0236 | | 0.0387 | 14.33 | 79200 | 0.1759 | 1.0222 | | 0.0387 | 14.35 | 79300 | 0.1787 | 1.0237 | | 0.0387 | 14.36 | 79400 | 0.1788 | 1.0227 | | 0.0372 | 14.38 | 79500 | 0.1789 | 1.0232 | | 0.0372 | 14.4 | 79600 | 0.1771 | 1.0254 | | 0.0372 | 14.42 | 79700 | 0.1777 | 1.0244 | | 0.0372 | 14.44 | 79800 | 0.1791 | 1.0225 | | 0.0372 | 14.45 | 79900 | 0.1786 | 1.0237 | | 0.0385 | 14.47 | 80000 | 0.1782 | 1.0243 | | 0.0385 | 14.49 | 80100 | 0.1770 | 1.0236 | | 0.0385 | 14.51 | 80200 | 0.1782 | 1.0240 | | 0.0385 | 14.53 | 80300 | 0.1764 | 1.0243 | | 0.0385 | 14.54 | 80400 | 0.1748 | 1.0248 | | 0.039 | 14.56 | 80500 | 0.1758 | 1.0232 | | 0.039 | 14.58 | 80600 | 0.1763 | 1.0246 | | 0.039 | 14.6 | 80700 | 0.1770 | 1.0220 | | 0.039 | 14.62 | 80800 | 0.1788 | 1.0225 | | 0.039 | 14.63 | 80900 | 0.1781 | 1.0230 | | 0.039 | 14.65 | 81000 | 0.1779 | 1.0230 | | 0.039 | 14.67 | 81100 | 0.1755 | 1.0212 | | 0.039 | 14.69 | 81200 | 0.1765 | 1.0226 | | 0.039 | 14.71 | 81300 | 0.1787 | 1.0241 | | 0.039 | 14.72 | 81400 | 0.1782 | 1.0250 | | 0.0368 | 14.74 | 81500 | 0.1780 | 1.0248 | | 0.0368 | 14.76 | 81600 | 0.1782 | 1.0242 | | 0.0368 | 14.78 | 81700 | 0.1782 | 1.0242 | | 0.0368 | 14.8 | 81800 | 0.1792 | 1.0241 | | 0.0368 | 14.82 | 81900 | 0.1796 | 1.0238 | | 0.0378 | 14.83 | 82000 | 0.1795 | 1.0236 | | 0.0378 | 14.85 | 82100 | 0.1796 | 1.0239 | | 0.0378 | 14.87 | 82200 | 0.1792 | 1.0236 | | 0.0378 | 14.89 | 82300 | 0.1789 | 1.0239 | | 0.0378 | 14.91 | 82400 | 0.1788 | 1.0238 | | 0.0386 | 14.92 | 82500 | 0.1787 | 1.0239 | | 0.0386 | 14.94 | 82600 | 0.1786 | 1.0236 | | 0.0386 | 14.96 | 82700 | 0.1786 | 1.0237 | | 0.0386 | 14.98 | 82800 | 0.1787 | 1.0239 | | 0.0386 | 15.0 | 82900 | 0.1788 | 1.0238 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1 - Datasets 1.17.0 - Tokenizers 0.10.3

CLAck/en-km

[ "pytorch", "marian", "text2text-generation", "transformers", "translation", "autotrain_compatible" ]

translation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

12

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--- tags: - conversational - tagalog - filipino language: - tl --- # Tagalog DialoGPT This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium). This model is trained on 52K original conversations and 52K synthetic conversations, where 10% of tokens in each utterance in the synthetic conversation are machine-generated tokens.

CLAck/en-vi

[ "pytorch", "marian", "text2text-generation", "en", "vi", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]

translation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

8

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--- tags: - conversational - tagalog - filipino inference: false language: - tl --- # Tagalog DialoGPT This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium). This model is trained on 52K original conversations and 52K synthetic conversations, where 20% of tokens in each utterance in the synthetic conversation are machine-generated tokens.

CLAck/indo-mixed

[ "pytorch", "marian", "text2text-generation", "en", "id", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]

translation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

15

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--- tags: - conversational - tagalog - filipino inference: false language: - tl --- # Tagalog DialoGPT This is an extension of the base Tagalog DialoGPT model (https://huggingface.co/gabtan99/dialogpt-tagalog-medium). This model is trained on 52K original conversations and 52K synthetic conversations, where 30% of tokens in each utterance in the synthetic conversation are machine-generated tokens.

CLAck/indo-pure

[ "pytorch", "marian", "text2text-generation", "en", "id", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]

translation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

4

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--- tags: - conversational - tagalog - filipino language: - tl inference: false datasets: - gabtan99/pex-conversations --- # Tagalog DialoGPT A DialoGPT-medium model fine-tuned on Tagalog conversational data scraped from the web. This model is an output of a research on RoBERTa-based data augmentation for low resource languages. This is the baseline model which did not use any synthetic data in training. # Latest release: July 25, 2021 * The model is currently only able to respond based on the history of 3 previous utterances before being limited. This is a result of the scarce amount of Tagalog conversations in our dataset. # Dataset [PEx Conversations Dataset](https://huggingface.co/datasets/gabtan99/pex-conversations) # Usage Here is an example of using beam search for model inference. ``` for step in range(2): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # we limit the generation to 512 tokens, each utterance in training had a maximum of 128 tokens chat_history_ids = model.generate( bot_input_ids, max_length=512, pad_token_id=tokenizer.eos_token_id, num_beams=5, no_repeat_ngram_size=3 ) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ``` # Training Script [Fine-tuning script adapted from Spanish DialoGPT](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb) # Research by * [tyadrianpaule](https://huggingface.co/tyadrianpaule) * [schuylerng](https://huggingface.co/schuylerng) * [dcl127](https://huggingface.co/dcl127)

CLAck/vi-en

[ "pytorch", "marian", "text2text-generation", "en", "vi", "dataset:ALT", "transformers", "translation", "license:apache-2.0", "autotrain_compatible" ]

translation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

6

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I am adding my first README in order to test the interface. How good is it really?

CLEE/CLEE

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 --- This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```

CLTL/MedRoBERTa.nl

[ "pytorch", "roberta", "fill-mask", "nl", "transformers", "license:mit", "autotrain_compatible" ]

fill-mask

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2,988

null

--- license: apache-2.0 --- This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```

CLTL/gm-ner-xlmrbase

[ "pytorch", "tf", "xlm-roberta", "token-classification", "nl", "transformers", "dighum", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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2

null

--- license: apache-2.0 --- This model is used in the paper **Generative Relation Linking for Question Answering over Knowledge Bases**. [ArXiv](https://arxiv.org/abs/2108.07337), [GitHub](https://github.com/IBM/kbqa-relation-linking) ## Citation ```bibtex @inproceedings{rossiello-genrl-2021, title={Generative relation linking for question answering over knowledge bases}, author={Rossiello, Gaetano and Mihindukulasooriya, Nandana and Abdelaziz, Ibrahim and Bornea, Mihaela and Gliozzo, Alfio and Naseem, Tahira and Kapanipathi, Pavan}, booktitle={International Semantic Web Conference}, pages={321--337}, year={2021}, organization={Springer}, url = "https://link.springer.com/chapter/10.1007/978-3-030-88361-4_19", doi = "10.1007/978-3-030-88361-4_19" } ```

Cameron/BERT-SBIC-offensive

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

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31

null

# Leetcode using AI :robot: GPT-2 Model for Leetcode Questions in python **Note**: the Answers might not make sense in some cases because of the bias in GPT-2 **Contribtuions:** If you would like to make the model better contributions are welcome Check out [CONTRIBUTIONS.md](https://github.com/gagan3012/project-code-py/blob/master/CONTRIBUTIONS.md) ### 📢 Favour: It would be highly motivating, if you can STAR⭐ this repo if you find it helpful. ## Model Two models have been developed for different use cases and they can be found at https://huggingface.co/gagan3012 The model weights can be found here: [GPT-2](https://huggingface.co/gagan3012/project-code-py) and [DistilGPT-2](https://huggingface.co/gagan3012/project-code-py-small) ### Example usage: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("gagan3012/project-code-py") model = AutoModelWithLMHead.from_pretrained("gagan3012/project-code-py") ``` ## Demo [![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/gagan3012/project-code-py/app.py) A streamlit webapp has been setup to use the model: https://share.streamlit.io/gagan3012/project-code-py/app.py ![image](https://user-images.githubusercontent.com/49101362/110356042-e69e4780-804a-11eb-94c6-a47fa3cd66b3.png) ## Example results: ### Question: ``` Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list. ``` ### Answer: ```python """ Write a function to delete a node in a singly-linked list. You will not be given access to the head of the list, instead you will be given access to the node to be deleted directly. It is guaranteed that the node to be deleted is not a tail node in the list. For example, a = 1->2->3 b = 3->1->2 t = ListNode(-1, 1) Note: The lexicographic ordering of the nodes in a tree matters. Do not assign values to nodes in a tree. Example 1: Input: [1,2,3] Output: 1->2->5 Explanation: 1->2->3->3->4, then 1->2->5[2] and then 5->1->3->4. Note: The length of a linked list will be in the range [1, 1000]. Node.val must be a valid LinkedListNode type. Both the length and the value of the nodes in a linked list will be in the range [-1000, 1000]. All nodes are distinct. """ # Definition for singly-linked list. # class ListNode: # def __init__(self, x): # self.val = x # self.next = None class Solution: def deleteNode(self, head: ListNode, val: int) -> None: """ BFS Linked List :param head: ListNode :param val: int :return: ListNode """ if head is not None: return head dummy = ListNode(-1, 1) dummy.next = head dummy.next.val = val dummy.next.next = head dummy.val = "" s1 = Solution() print(s1.deleteNode(head)) print(s1.deleteNode(-1)) print(s1.deleteNode(-1)) ```

Capreolus/bert-base-msmarco

[ "pytorch", "tf", "jax", "bert", "text-classification", "arxiv:2008.09093", "transformers" ]

text-classification

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238

null

--- language: - hi-en tags: - sentiment - multilingual - hindi codemix - hinglish license: apache-2.0 datasets: - sail --- # Sentiment Classification for hinglish text: `gk-hinglish-sentiment` ## Model description Trained small amount of reviews dataset ## Intended uses & limitations I wanted something to work well with hinglish data as it is being used in India mostly. The training data was not much as expected #### How to use ```python #sample code from transformers import BertTokenizer, BertForSequenceClassification tokenizerg = BertTokenizer.from_pretrained("/content/model") modelg = BertForSequenceClassification.from_pretrained("/content/model") text = "kuch bhi type karo hinglish mai" encoded_input = tokenizerg(text, return_tensors='pt') output = modelg(**encoded_input) print(output) #output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive ``` #### Limitations and bias The data contains only hinglish codemixed text it and was very much limited may be I will Update this model if I can get good amount of data ## Training data Training data contains labeled data for 3 labels link to the pre-trained model card with description of the pre-training data. I have Tuned below model https://huggingface.co/rohanrajpal/bert-base-multilingual-codemixed-cased-sentiment ### BibTeX entry and citation info ```@inproceedings{khanuja-etal-2020-gluecos, title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}", author = "Khanuja, Simran and Dandapat, Sandipan and Srinivasan, Anirudh and Sitaram, Sunayana and Choudhury, Monojit", booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.acl-main.329", pages = "3575--3585" } ```

dccuchile/albert-base-spanish-finetuned-xnli

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

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28

null

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # gaussfer/test_simcse_new This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('gaussfer/test_simcse_new') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('gaussfer/test_simcse_new') model = AutoModel.from_pretrained('gaussfer/test_simcse_new') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=gaussfer/test_simcse_new) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 875 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 40, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

dccuchile/albert-large-spanish-finetuned-pos

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

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1

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-finetuned-pubmed results: [] ---  # bart-finetuned-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5363 - Rouge2 Precision: 0.3459 - Rouge2 Recall: 0.2455 - Rouge2 Fmeasure: 0.2731 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.652 | 1.0 | 1125 | 1.5087 | 0.3647 | 0.2425 | 0.2772 | | 1.4695 | 2.0 | 2250 | 1.5039 | 0.3448 | 0.2457 | 0.2732 | | 1.3714 | 3.0 | 3375 | 1.4842 | 0.3509 | 0.2474 | 0.277 | | 1.2734 | 4.0 | 4500 | 1.4901 | 0.3452 | 0.2426 | 0.2716 | | 1.1853 | 5.0 | 5625 | 1.5152 | 0.3658 | 0.2371 | 0.2744 | | 1.0975 | 6.0 | 6750 | 1.5133 | 0.3529 | 0.2417 | 0.2729 | | 1.0448 | 7.0 | 7875 | 1.5203 | 0.3485 | 0.2464 | 0.275 | | 0.9999 | 8.0 | 9000 | 1.5316 | 0.3437 | 0.2435 | 0.2719 | | 0.9732 | 9.0 | 10125 | 1.5338 | 0.3464 | 0.2446 | 0.2732 | | 0.954 | 10.0 | 11250 | 1.5363 | 0.3459 | 0.2455 | 0.2731 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-large-spanish-finetuned-qa-mlqa

[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-15 results: [] ---  # bart-mlm-pubmed-15 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4822 - Rouge2 Precision: 0.7578 - Rouge2 Recall: 0.5933 - Rouge2 Fmeasure: 0.6511 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.7006 | 1.0 | 663 | 0.5062 | 0.7492 | 0.5855 | 0.6434 | | 0.5709 | 2.0 | 1326 | 0.4811 | 0.7487 | 0.5879 | 0.6447 | | 0.5011 | 3.0 | 1989 | 0.4734 | 0.7541 | 0.5906 | 0.6483 | | 0.4164 | 4.0 | 2652 | 0.4705 | 0.7515 | 0.5876 | 0.6452 | | 0.3888 | 5.0 | 3315 | 0.4703 | 0.7555 | 0.5946 | 0.6515 | | 0.3655 | 6.0 | 3978 | 0.4725 | 0.7572 | 0.5943 | 0.6516 | | 0.319 | 7.0 | 4641 | 0.4733 | 0.7557 | 0.5911 | 0.6491 | | 0.3089 | 8.0 | 5304 | 0.4792 | 0.7577 | 0.5936 | 0.6513 | | 0.2907 | 9.0 | 5967 | 0.4799 | 0.7577 | 0.5931 | 0.6509 | | 0.275 | 10.0 | 6630 | 0.4822 | 0.7578 | 0.5933 | 0.6511 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-tiny-spanish-finetuned-mldoc

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

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32

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-35 results: [] ---  # bart-mlm-pubmed-35 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9359 - Rouge2 Precision: 0.5451 - Rouge2 Recall: 0.4232 - Rouge2 Fmeasure: 0.4666 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.4156 | 1.0 | 663 | 1.0366 | 0.5165 | 0.3967 | 0.4394 | | 1.1773 | 2.0 | 1326 | 0.9841 | 0.5354 | 0.4168 | 0.4589 | | 1.0894 | 3.0 | 1989 | 0.9554 | 0.5346 | 0.4133 | 0.4563 | | 0.9359 | 4.0 | 2652 | 0.9440 | 0.5357 | 0.4163 | 0.4587 | | 0.8758 | 5.0 | 3315 | 0.9340 | 0.5428 | 0.4226 | 0.465 | | 0.8549 | 6.0 | 3978 | 0.9337 | 0.5385 | 0.422 | 0.4634 | | 0.7743 | 7.0 | 4641 | 0.9330 | 0.542 | 0.422 | 0.4647 | | 0.7465 | 8.0 | 5304 | 0.9315 | 0.5428 | 0.4231 | 0.4654 | | 0.7348 | 9.0 | 5967 | 0.9344 | 0.5462 | 0.4244 | 0.4674 | | 0.7062 | 10.0 | 6630 | 0.9359 | 0.5451 | 0.4232 | 0.4666 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-tiny-spanish-finetuned-ner

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

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8

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-45 results: [] ---  # bart-mlm-pubmed-45 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1797 - Rouge2 Precision: 0.4333 - Rouge2 Recall: 0.3331 - Rouge2 Fmeasure: 0.3684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.7989 | 1.0 | 663 | 1.3385 | 0.4097 | 0.3086 | 0.3444 | | 1.5072 | 2.0 | 1326 | 1.2582 | 0.4218 | 0.3213 | 0.3569 | | 1.4023 | 3.0 | 1989 | 1.2236 | 0.4207 | 0.3211 | 0.3562 | | 1.2205 | 4.0 | 2652 | 1.2025 | 0.4359 | 0.3331 | 0.3696 | | 1.1584 | 5.0 | 3315 | 1.1910 | 0.4304 | 0.3307 | 0.3658 | | 1.1239 | 6.0 | 3978 | 1.1830 | 0.4247 | 0.3279 | 0.3618 | | 1.0384 | 7.0 | 4641 | 1.1761 | 0.4308 | 0.3325 | 0.367 | | 1.0168 | 8.0 | 5304 | 1.1762 | 0.4314 | 0.3336 | 0.368 | | 0.9966 | 9.0 | 5967 | 1.1773 | 0.4335 | 0.3341 | 0.369 | | 0.961 | 10.0 | 6630 | 1.1797 | 0.4333 | 0.3331 | 0.3684 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-tiny-spanish-finetuned-pawsx

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

29

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--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed-medterm results: [] ---  # bart-mlm-pubmed-medterm This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Rouge2 Precision: 0.985 - Rouge2 Recall: 0.7208 - Rouge2 Fmeasure: 0.8088 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.0018 | 1.0 | 13833 | 0.0003 | 0.985 | 0.7208 | 0.8088 | | 0.0014 | 2.0 | 27666 | 0.0006 | 0.9848 | 0.7207 | 0.8086 | | 0.0009 | 3.0 | 41499 | 0.0002 | 0.9848 | 0.7207 | 0.8086 | | 0.0007 | 4.0 | 55332 | 0.0002 | 0.985 | 0.7208 | 0.8088 | | 0.0006 | 5.0 | 69165 | 0.0001 | 0.9848 | 0.7207 | 0.8087 | | 0.0001 | 6.0 | 82998 | 0.0002 | 0.9846 | 0.7206 | 0.8086 | | 0.0009 | 7.0 | 96831 | 0.0001 | 0.9848 | 0.7208 | 0.8087 | | 0.0 | 8.0 | 110664 | 0.0000 | 0.9848 | 0.7207 | 0.8087 | | 0.0001 | 9.0 | 124497 | 0.0000 | 0.985 | 0.7208 | 0.8088 | | 0.0 | 10.0 | 138330 | 0.0000 | 0.985 | 0.7208 | 0.8088 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

dccuchile/albert-tiny-spanish-finetuned-pos

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

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--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-mlm-pubmed results: [] ---  # bart-mlm-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7223 - Rouge2 Precision: 0.6572 - Rouge2 Recall: 0.5164 - Rouge2 Fmeasure: 0.5662 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.0322 | 1.0 | 663 | 0.7891 | 0.639 | 0.4989 | 0.5491 | | 0.8545 | 2.0 | 1326 | 0.7433 | 0.6461 | 0.5057 | 0.5556 | | 0.758 | 3.0 | 1989 | 0.7299 | 0.647 | 0.5033 | 0.5547 | | 0.6431 | 4.0 | 2652 | 0.7185 | 0.6556 | 0.5101 | 0.5616 | | 0.6058 | 5.0 | 3315 | 0.7126 | 0.6537 | 0.5144 | 0.5638 | | 0.5726 | 6.0 | 3978 | 0.7117 | 0.6567 | 0.5169 | 0.5666 | | 0.5168 | 7.0 | 4641 | 0.7150 | 0.6585 | 0.5154 | 0.566 | | 0.5011 | 8.0 | 5304 | 0.7220 | 0.6568 | 0.5164 | 0.5664 | | 0.4803 | 9.0 | 5967 | 0.7208 | 0.6573 | 0.5161 | 0.5662 | | 0.4577 | 10.0 | 6630 | 0.7223 | 0.6572 | 0.5164 | 0.5662 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-tiny-spanish-finetuned-qa-mlqa

[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

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--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-paraphrase-pubmed-1.1 results: [] ---  # bart-paraphrase-pubmed-1.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4236 - Rouge2 Precision: 0.8482 - Rouge2 Recall: 0.673 - Rouge2 Fmeasure: 0.7347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.6534 | 1.0 | 663 | 0.4641 | 0.8448 | 0.6691 | 0.7313 | | 0.5078 | 2.0 | 1326 | 0.4398 | 0.8457 | 0.6719 | 0.7333 | | 0.4367 | 3.0 | 1989 | 0.4274 | 0.847 | 0.6717 | 0.7335 | | 0.3575 | 4.0 | 2652 | 0.4149 | 0.8481 | 0.6733 | 0.735 | | 0.3319 | 5.0 | 3315 | 0.4170 | 0.8481 | 0.6724 | 0.7343 | | 0.3179 | 6.0 | 3978 | 0.4264 | 0.8484 | 0.6733 | 0.735 | | 0.2702 | 7.0 | 4641 | 0.4207 | 0.8489 | 0.6732 | 0.7353 | | 0.2606 | 8.0 | 5304 | 0.4205 | 0.8487 | 0.6725 | 0.7347 | | 0.2496 | 9.0 | 5967 | 0.4247 | 0.8466 | 0.6717 | 0.7334 | | 0.2353 | 10.0 | 6630 | 0.4236 | 0.8482 | 0.673 | 0.7347 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-tiny-spanish-finetuned-xnli

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

31

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--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-paraphrase-pubmed results: [] ---  # bart-paraphrase-pubmed This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6340 - Rouge2 Precision: 0.83 - Rouge2 Recall: 0.6526 - Rouge2 Fmeasure: 0.7144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.6613 | 1.0 | 663 | 0.4750 | 0.8321 | 0.6552 | 0.7167 | | 0.4993 | 2.0 | 1326 | 0.4404 | 0.8366 | 0.6583 | 0.7203 | | 0.443 | 3.0 | 1989 | 0.4261 | 0.8319 | 0.6562 | 0.7176 | | 0.3482 | 4.0 | 2652 | 0.4198 | 0.8348 | 0.6571 | 0.7191 | | 0.3206 | 5.0 | 3315 | 0.4233 | 0.8344 | 0.656 | 0.7183 | | 0.294 | 6.0 | 3978 | 0.4334 | 0.835 | 0.657 | 0.719 | | 0.2404 | 7.0 | 4641 | 0.4437 | 0.8334 | 0.6559 | 0.7178 | | 0.2228 | 8.0 | 5304 | 0.4438 | 0.8348 | 0.6565 | 0.7187 | | 0.211 | 9.0 | 5967 | 0.4516 | 0.8329 | 0.6549 | 0.717 | | 0.1713 | 10.0 | 6630 | 0.4535 | 0.8332 | 0.6547 | 0.7169 | | 0.1591 | 11.0 | 7293 | 0.4763 | 0.8349 | 0.6561 | 0.7184 | | 0.1555 | 12.0 | 7956 | 0.4824 | 0.8311 | 0.6534 | 0.7153 | | 0.1262 | 13.0 | 8619 | 0.4883 | 0.8322 | 0.655 | 0.7167 | | 0.1164 | 14.0 | 9282 | 0.5025 | 0.8312 | 0.6539 | 0.7158 | | 0.1108 | 15.0 | 9945 | 0.5149 | 0.8321 | 0.6535 | 0.7157 | | 0.0926 | 16.0 | 10608 | 0.5340 | 0.8315 | 0.6544 | 0.7159 | | 0.0856 | 17.0 | 11271 | 0.5322 | 0.8306 | 0.6518 | 0.7142 | | 0.0785 | 18.0 | 11934 | 0.5346 | 0.8324 | 0.6549 | 0.7167 | | 0.071 | 19.0 | 12597 | 0.5488 | 0.8311 | 0.652 | 0.714 | | 0.0635 | 20.0 | 13260 | 0.5624 | 0.8287 | 0.6517 | 0.7132 | | 0.0608 | 21.0 | 13923 | 0.5612 | 0.8299 | 0.6527 | 0.7141 | | 0.0531 | 22.0 | 14586 | 0.5764 | 0.8283 | 0.6498 | 0.7119 | | 0.0486 | 23.0 | 15249 | 0.5832 | 0.8298 | 0.6532 | 0.7148 | | 0.0465 | 24.0 | 15912 | 0.5866 | 0.83 | 0.6522 | 0.7142 | | 0.0418 | 25.0 | 16575 | 0.5825 | 0.83 | 0.6523 | 0.7141 | | 0.0391 | 26.0 | 17238 | 0.5997 | 0.8306 | 0.6545 | 0.716 | | 0.0376 | 27.0 | 17901 | 0.5894 | 0.8315 | 0.6546 | 0.7164 | | 0.035 | 28.0 | 18564 | 0.6045 | 0.8306 | 0.6529 | 0.7149 | | 0.0316 | 29.0 | 19227 | 0.6168 | 0.8311 | 0.6546 | 0.7162 | | 0.0314 | 30.0 | 19890 | 0.6203 | 0.8311 | 0.6552 | 0.7164 | | 0.0292 | 31.0 | 20553 | 0.6173 | 0.8315 | 0.6548 | 0.7163 | | 0.0265 | 32.0 | 21216 | 0.6226 | 0.832 | 0.6548 | 0.7166 | | 0.0274 | 33.0 | 21879 | 0.6264 | 0.8314 | 0.6538 | 0.7155 | | 0.0247 | 34.0 | 22542 | 0.6254 | 0.8289 | 0.6515 | 0.7132 | | 0.0238 | 35.0 | 23205 | 0.6254 | 0.8307 | 0.6519 | 0.7142 | | 0.0232 | 36.0 | 23868 | 0.6295 | 0.8287 | 0.6515 | 0.7133 | | 0.0215 | 37.0 | 24531 | 0.6326 | 0.8293 | 0.6523 | 0.7138 | | 0.0212 | 38.0 | 25194 | 0.6332 | 0.8295 | 0.6522 | 0.714 | | 0.0221 | 39.0 | 25857 | 0.6335 | 0.8305 | 0.6528 | 0.7147 | | 0.0202 | 40.0 | 26520 | 0.6340 | 0.83 | 0.6526 | 0.7144 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-xlarge-spanish-finetuned-ner

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

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--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-pubmed results: [] ---  # t5-small-finetuned-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6131 - Rouge2 Precision: 0.3 - Rouge2 Recall: 0.2152 - Rouge2 Fmeasure: 0.2379 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 2.1335 | 1.0 | 563 | 1.7632 | 0.2716 | 0.1936 | 0.2135 | | 1.9373 | 2.0 | 1126 | 1.7037 | 0.2839 | 0.2068 | 0.2265 | | 1.8827 | 3.0 | 1689 | 1.6723 | 0.2901 | 0.2118 | 0.2316 | | 1.8257 | 4.0 | 2252 | 1.6503 | 0.2938 | 0.2115 | 0.2332 | | 1.8152 | 5.0 | 2815 | 1.6386 | 0.2962 | 0.2139 | 0.2357 | | 1.7939 | 6.0 | 3378 | 1.6284 | 0.2976 | 0.212 | 0.2354 | | 1.7845 | 7.0 | 3941 | 1.6211 | 0.2991 | 0.2155 | 0.2383 | | 1.7468 | 8.0 | 4504 | 1.6167 | 0.2994 | 0.217 | 0.239 | | 1.7464 | 9.0 | 5067 | 1.6137 | 0.3007 | 0.2154 | 0.2382 | | 1.744 | 10.0 | 5630 | 1.6131 | 0.3 | 0.2152 | 0.2379 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-xlarge-spanish-finetuned-pawsx

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

24

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--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed-15 results: [] ---  # t5-small-mlm-pubmed-15 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5389 - Rouge2 Precision: 0.7165 - Rouge2 Recall: 0.5375 - Rouge2 Fmeasure: 0.5981 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.1024 | 0.75 | 500 | 0.7890 | 0.6854 | 0.4813 | 0.5502 | | 0.8788 | 1.51 | 1000 | 0.7176 | 0.6906 | 0.4989 | 0.5638 | | 0.8086 | 2.26 | 1500 | 0.6830 | 0.6872 | 0.5052 | 0.5663 | | 0.7818 | 3.02 | 2000 | 0.6650 | 0.6912 | 0.5104 | 0.5711 | | 0.7466 | 3.77 | 2500 | 0.6458 | 0.6965 | 0.5167 | 0.5774 | | 0.731 | 4.52 | 3000 | 0.6355 | 0.6955 | 0.5161 | 0.5763 | | 0.7126 | 5.28 | 3500 | 0.6249 | 0.6924 | 0.517 | 0.576 | | 0.6998 | 6.03 | 4000 | 0.6166 | 0.6995 | 0.5207 | 0.5809 | | 0.6855 | 6.79 | 4500 | 0.6076 | 0.6981 | 0.5215 | 0.5813 | | 0.676 | 7.54 | 5000 | 0.6015 | 0.7003 | 0.5242 | 0.5836 | | 0.6688 | 8.3 | 5500 | 0.5962 | 0.7004 | 0.5235 | 0.583 | | 0.6569 | 9.05 | 6000 | 0.5900 | 0.6997 | 0.5234 | 0.5827 | | 0.6503 | 9.8 | 6500 | 0.5880 | 0.703 | 0.5257 | 0.5856 | | 0.6455 | 10.56 | 7000 | 0.5818 | 0.7008 | 0.5259 | 0.5849 | | 0.635 | 11.31 | 7500 | 0.5796 | 0.7017 | 0.5271 | 0.5861 | | 0.6323 | 12.07 | 8000 | 0.5769 | 0.7053 | 0.5276 | 0.5877 | | 0.6241 | 12.82 | 8500 | 0.5730 | 0.7011 | 0.5243 | 0.5838 | | 0.6224 | 13.57 | 9000 | 0.5696 | 0.7046 | 0.5286 | 0.5879 | | 0.6139 | 14.33 | 9500 | 0.5685 | 0.7047 | 0.5295 | 0.5886 | | 0.6118 | 15.08 | 10000 | 0.5653 | 0.704 | 0.5297 | 0.5886 | | 0.6089 | 15.84 | 10500 | 0.5633 | 0.703 | 0.5272 | 0.5865 | | 0.598 | 16.59 | 11000 | 0.5613 | 0.7059 | 0.5293 | 0.5889 | | 0.6003 | 17.35 | 11500 | 0.5602 | 0.7085 | 0.532 | 0.5918 | | 0.5981 | 18.1 | 12000 | 0.5587 | 0.7106 | 0.5339 | 0.5938 | | 0.5919 | 18.85 | 12500 | 0.5556 | 0.708 | 0.5319 | 0.5914 | | 0.5897 | 19.61 | 13000 | 0.5556 | 0.7106 | 0.5327 | 0.5931 | | 0.5899 | 20.36 | 13500 | 0.5526 | 0.7114 | 0.534 | 0.5939 | | 0.5804 | 21.12 | 14000 | 0.5521 | 0.7105 | 0.5328 | 0.5928 | | 0.5764 | 21.87 | 14500 | 0.5520 | 0.715 | 0.537 | 0.5976 | | 0.5793 | 22.62 | 15000 | 0.5506 | 0.713 | 0.5346 | 0.5951 | | 0.5796 | 23.38 | 15500 | 0.5492 | 0.7124 | 0.5352 | 0.5952 | | 0.5672 | 24.13 | 16000 | 0.5482 | 0.7124 | 0.5346 | 0.5948 | | 0.5737 | 24.89 | 16500 | 0.5470 | 0.7134 | 0.5352 | 0.5956 | | 0.5685 | 25.64 | 17000 | 0.5463 | 0.7117 | 0.5346 | 0.5946 | | 0.5658 | 26.4 | 17500 | 0.5457 | 0.7145 | 0.5359 | 0.5965 | | 0.5657 | 27.15 | 18000 | 0.5447 | 0.7145 | 0.5367 | 0.597 | | 0.5645 | 27.9 | 18500 | 0.5441 | 0.7141 | 0.5362 | 0.5964 | | 0.565 | 28.66 | 19000 | 0.5436 | 0.7151 | 0.5367 | 0.5972 | | 0.5579 | 29.41 | 19500 | 0.5426 | 0.7162 | 0.5378 | 0.5982 | | 0.563 | 30.17 | 20000 | 0.5424 | 0.7155 | 0.5373 | 0.5977 | | 0.556 | 30.92 | 20500 | 0.5418 | 0.7148 | 0.536 | 0.5966 | | 0.5576 | 31.67 | 21000 | 0.5411 | 0.7141 | 0.5356 | 0.5961 | | 0.5546 | 32.43 | 21500 | 0.5409 | 0.7149 | 0.5364 | 0.5967 | | 0.556 | 33.18 | 22000 | 0.5405 | 0.7143 | 0.5356 | 0.596 | | 0.5536 | 33.94 | 22500 | 0.5401 | 0.7165 | 0.5377 | 0.5982 | | 0.5527 | 34.69 | 23000 | 0.5397 | 0.7188 | 0.5389 | 0.5999 | | 0.5531 | 35.44 | 23500 | 0.5395 | 0.7172 | 0.538 | 0.5989 | | 0.5508 | 36.2 | 24000 | 0.5392 | 0.7166 | 0.538 | 0.5985 | | 0.5495 | 36.95 | 24500 | 0.5391 | 0.7176 | 0.5387 | 0.5993 | | 0.5539 | 37.71 | 25000 | 0.5391 | 0.7169 | 0.5372 | 0.598 | | 0.5452 | 38.46 | 25500 | 0.5390 | 0.7179 | 0.5384 | 0.5991 | | 0.5513 | 39.22 | 26000 | 0.5390 | 0.717 | 0.5377 | 0.5984 | | 0.5506 | 39.97 | 26500 | 0.5389 | 0.7165 | 0.5375 | 0.5981 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-xlarge-spanish-finetuned-pos

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

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3

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed-35 results: [] ---  # t5-small-mlm-pubmed-35 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1101 - Rouge2 Precision: 0.4758 - Rouge2 Recall: 0.3498 - Rouge2 Fmeasure: 0.3927 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.8404 | 0.75 | 500 | 1.5005 | 0.4265 | 0.2786 | 0.3273 | | 1.6858 | 1.51 | 1000 | 1.4216 | 0.4318 | 0.2946 | 0.3404 | | 1.6071 | 2.26 | 1500 | 1.3777 | 0.4472 | 0.3148 | 0.3598 | | 1.5551 | 3.02 | 2000 | 1.3360 | 0.4406 | 0.3168 | 0.3586 | | 1.5116 | 3.77 | 2500 | 1.3128 | 0.4523 | 0.3234 | 0.3671 | | 1.4837 | 4.52 | 3000 | 1.2937 | 0.4477 | 0.3215 | 0.3645 | | 1.4513 | 5.28 | 3500 | 1.2766 | 0.4511 | 0.3262 | 0.3689 | | 1.4336 | 6.03 | 4000 | 1.2626 | 0.4548 | 0.3283 | 0.3718 | | 1.4149 | 6.79 | 4500 | 1.2449 | 0.4495 | 0.3274 | 0.3687 | | 1.3977 | 7.54 | 5000 | 1.2349 | 0.4507 | 0.3305 | 0.3712 | | 1.3763 | 8.3 | 5500 | 1.2239 | 0.4519 | 0.3266 | 0.3688 | | 1.371 | 9.05 | 6000 | 1.2171 | 0.4546 | 0.3305 | 0.3727 | | 1.3501 | 9.8 | 6500 | 1.2080 | 0.4575 | 0.3329 | 0.3755 | | 1.3443 | 10.56 | 7000 | 1.2017 | 0.4576 | 0.3314 | 0.3742 | | 1.326 | 11.31 | 7500 | 1.1926 | 0.4578 | 0.333 | 0.3757 | | 1.3231 | 12.07 | 8000 | 1.1866 | 0.4606 | 0.3357 | 0.3782 | | 1.3089 | 12.82 | 8500 | 1.1816 | 0.4591 | 0.3338 | 0.3765 | | 1.3007 | 13.57 | 9000 | 1.1764 | 0.4589 | 0.3361 | 0.3777 | | 1.2943 | 14.33 | 9500 | 1.1717 | 0.4641 | 0.3382 | 0.3811 | | 1.2854 | 15.08 | 10000 | 1.1655 | 0.4617 | 0.3378 | 0.38 | | 1.2777 | 15.84 | 10500 | 1.1612 | 0.464 | 0.3401 | 0.3823 | | 1.2684 | 16.59 | 11000 | 1.1581 | 0.4608 | 0.3367 | 0.3789 | | 1.2612 | 17.35 | 11500 | 1.1554 | 0.4623 | 0.3402 | 0.3818 | | 1.2625 | 18.1 | 12000 | 1.1497 | 0.4613 | 0.3381 | 0.3802 | | 1.2529 | 18.85 | 12500 | 1.1465 | 0.4671 | 0.3419 | 0.3848 | | 1.2461 | 19.61 | 13000 | 1.1431 | 0.4646 | 0.3399 | 0.3824 | | 1.2415 | 20.36 | 13500 | 1.1419 | 0.4659 | 0.341 | 0.3835 | | 1.2375 | 21.12 | 14000 | 1.1377 | 0.4693 | 0.3447 | 0.3873 | | 1.2315 | 21.87 | 14500 | 1.1353 | 0.4672 | 0.3433 | 0.3855 | | 1.2263 | 22.62 | 15000 | 1.1333 | 0.467 | 0.3433 | 0.3854 | | 1.2214 | 23.38 | 15500 | 1.1305 | 0.4682 | 0.3446 | 0.3869 | | 1.2202 | 24.13 | 16000 | 1.1291 | 0.4703 | 0.3465 | 0.3888 | | 1.2155 | 24.89 | 16500 | 1.1270 | 0.472 | 0.348 | 0.3903 | | 1.2064 | 25.64 | 17000 | 1.1261 | 0.4724 | 0.3479 | 0.3905 | | 1.2173 | 26.4 | 17500 | 1.1236 | 0.4734 | 0.3485 | 0.3912 | | 1.1994 | 27.15 | 18000 | 1.1220 | 0.4739 | 0.3486 | 0.3915 | | 1.2018 | 27.9 | 18500 | 1.1217 | 0.4747 | 0.3489 | 0.3921 | | 1.2045 | 28.66 | 19000 | 1.1194 | 0.4735 | 0.3488 | 0.3916 | | 1.1949 | 29.41 | 19500 | 1.1182 | 0.4732 | 0.3484 | 0.3911 | | 1.19 | 30.17 | 20000 | 1.1166 | 0.4724 | 0.3479 | 0.3904 | | 1.1932 | 30.92 | 20500 | 1.1164 | 0.4753 | 0.3494 | 0.3924 | | 1.1952 | 31.67 | 21000 | 1.1147 | 0.4733 | 0.3485 | 0.3911 | | 1.1922 | 32.43 | 21500 | 1.1146 | 0.475 | 0.3494 | 0.3923 | | 1.1889 | 33.18 | 22000 | 1.1132 | 0.4765 | 0.3499 | 0.3933 | | 1.1836 | 33.94 | 22500 | 1.1131 | 0.4768 | 0.351 | 0.3939 | | 1.191 | 34.69 | 23000 | 1.1127 | 0.4755 | 0.3495 | 0.3926 | | 1.1811 | 35.44 | 23500 | 1.1113 | 0.4748 | 0.349 | 0.3919 | | 1.1864 | 36.2 | 24000 | 1.1107 | 0.4751 | 0.3494 | 0.3921 | | 1.1789 | 36.95 | 24500 | 1.1103 | 0.4756 | 0.3499 | 0.3927 | | 1.1819 | 37.71 | 25000 | 1.1101 | 0.4758 | 0.35 | 0.3932 | | 1.1862 | 38.46 | 25500 | 1.1099 | 0.4755 | 0.3497 | 0.3926 | | 1.1764 | 39.22 | 26000 | 1.1101 | 0.4759 | 0.3498 | 0.3928 | | 1.1819 | 39.97 | 26500 | 1.1101 | 0.4758 | 0.3498 | 0.3927 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-xlarge-spanish-finetuned-qa-mlqa

[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed-45 results: [] ---  # t5-small-mlm-pubmed-45 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6395 - Rouge2 Precision: 0.3383 - Rouge2 Recall: 0.2424 - Rouge2 Fmeasure: 0.2753 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 2.519 | 0.75 | 500 | 1.9659 | 0.3178 | 0.1888 | 0.2299 | | 2.169 | 1.51 | 1000 | 1.8450 | 0.3256 | 0.2138 | 0.25 | | 2.0796 | 2.26 | 1500 | 1.7900 | 0.3368 | 0.2265 | 0.2636 | | 1.9978 | 3.02 | 2000 | 1.7553 | 0.3427 | 0.234 | 0.2709 | | 1.9686 | 3.77 | 2500 | 1.7172 | 0.3356 | 0.2347 | 0.2692 | | 1.9142 | 4.52 | 3000 | 1.6986 | 0.3358 | 0.238 | 0.2715 | | 1.921 | 5.28 | 3500 | 1.6770 | 0.3349 | 0.2379 | 0.2709 | | 1.8848 | 6.03 | 4000 | 1.6683 | 0.3346 | 0.2379 | 0.2708 | | 1.8674 | 6.79 | 4500 | 1.6606 | 0.3388 | 0.2419 | 0.2752 | | 1.8606 | 7.54 | 5000 | 1.6514 | 0.3379 | 0.2409 | 0.274 | | 1.8515 | 8.3 | 5500 | 1.6438 | 0.3356 | 0.2407 | 0.2731 | | 1.8403 | 9.05 | 6000 | 1.6401 | 0.3367 | 0.2421 | 0.2744 | | 1.8411 | 9.8 | 6500 | 1.6395 | 0.3383 | 0.2424 | 0.2753 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-xlarge-spanish-finetuned-xnli

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

29

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-mlm-pubmed results: [] ---  # t5-small-mlm-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8008 - Rouge2 Precision: 0.6071 - Rouge2 Recall: 0.4566 - Rouge2 Fmeasure: 0.5079 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.914 | 0.75 | 500 | 0.8691 | 0.5901 | 0.4357 | 0.4879 | | 0.9093 | 1.51 | 1000 | 0.8646 | 0.5867 | 0.4372 | 0.488 | | 0.895 | 2.26 | 1500 | 0.8618 | 0.5891 | 0.4387 | 0.49 | | 0.8842 | 3.02 | 2000 | 0.8571 | 0.5899 | 0.4374 | 0.4891 | | 0.8796 | 3.77 | 2500 | 0.8544 | 0.5903 | 0.4406 | 0.4916 | | 0.8759 | 4.52 | 3000 | 0.8513 | 0.5921 | 0.4395 | 0.4912 | | 0.8621 | 5.28 | 3500 | 0.8485 | 0.5934 | 0.4413 | 0.493 | | 0.8613 | 6.03 | 4000 | 0.8442 | 0.5944 | 0.4428 | 0.4944 | | 0.8537 | 6.79 | 4500 | 0.8406 | 0.594 | 0.4414 | 0.4932 | | 0.8518 | 7.54 | 5000 | 0.8399 | 0.5956 | 0.4424 | 0.4945 | | 0.8438 | 8.3 | 5500 | 0.8365 | 0.5953 | 0.4452 | 0.4964 | | 0.8339 | 9.05 | 6000 | 0.8353 | 0.5983 | 0.4468 | 0.4983 | | 0.8307 | 9.8 | 6500 | 0.8331 | 0.5979 | 0.4461 | 0.4976 | | 0.8328 | 10.56 | 7000 | 0.8304 | 0.5975 | 0.4465 | 0.4979 | | 0.8263 | 11.31 | 7500 | 0.8283 | 0.5977 | 0.4467 | 0.4981 | | 0.8168 | 12.07 | 8000 | 0.8267 | 0.5971 | 0.4463 | 0.4976 | | 0.8165 | 12.82 | 8500 | 0.8248 | 0.5969 | 0.4462 | 0.4976 | | 0.8084 | 13.57 | 9000 | 0.8245 | 0.6018 | 0.4527 | 0.5035 | | 0.8136 | 14.33 | 9500 | 0.8219 | 0.6023 | 0.4509 | 0.5023 | | 0.8073 | 15.08 | 10000 | 0.8206 | 0.6002 | 0.4486 | 0.5001 | | 0.808 | 15.84 | 10500 | 0.8185 | 0.6009 | 0.4506 | 0.5019 | | 0.8027 | 16.59 | 11000 | 0.8173 | 0.5978 | 0.4478 | 0.4989 | | 0.8061 | 17.35 | 11500 | 0.8169 | 0.6022 | 0.4513 | 0.5026 | | 0.7922 | 18.1 | 12000 | 0.8152 | 0.6016 | 0.4501 | 0.5016 | | 0.7928 | 18.85 | 12500 | 0.8141 | 0.6009 | 0.45 | 0.5012 | | 0.7909 | 19.61 | 13000 | 0.8143 | 0.6019 | 0.4521 | 0.5028 | | 0.7909 | 20.36 | 13500 | 0.8115 | 0.5997 | 0.4505 | 0.5011 | | 0.7949 | 21.12 | 14000 | 0.8115 | 0.6043 | 0.4536 | 0.5048 | | 0.7853 | 21.87 | 14500 | 0.8095 | 0.6033 | 0.4527 | 0.5038 | | 0.7819 | 22.62 | 15000 | 0.8095 | 0.6054 | 0.4541 | 0.5056 | | 0.7828 | 23.38 | 15500 | 0.8075 | 0.6036 | 0.453 | 0.5042 | | 0.787 | 24.13 | 16000 | 0.8068 | 0.6031 | 0.4528 | 0.504 | | 0.7739 | 24.89 | 16500 | 0.8072 | 0.6043 | 0.4529 | 0.5045 | | 0.7782 | 25.64 | 17000 | 0.8073 | 0.606 | 0.4551 | 0.5063 | | 0.7772 | 26.4 | 17500 | 0.8063 | 0.6055 | 0.4549 | 0.5062 | | 0.7718 | 27.15 | 18000 | 0.8057 | 0.606 | 0.4546 | 0.5059 | | 0.7747 | 27.9 | 18500 | 0.8045 | 0.6046 | 0.4543 | 0.5054 | | 0.7738 | 28.66 | 19000 | 0.8035 | 0.6059 | 0.4549 | 0.506 | | 0.7642 | 29.41 | 19500 | 0.8041 | 0.6053 | 0.4545 | 0.5058 | | 0.7666 | 30.17 | 20000 | 0.8039 | 0.6066 | 0.457 | 0.508 | | 0.7686 | 30.92 | 20500 | 0.8027 | 0.6075 | 0.4571 | 0.5081 | | 0.7664 | 31.67 | 21000 | 0.8026 | 0.6062 | 0.4566 | 0.5076 | | 0.77 | 32.43 | 21500 | 0.8022 | 0.6068 | 0.4571 | 0.5081 | | 0.7618 | 33.18 | 22000 | 0.8015 | 0.6065 | 0.4563 | 0.5072 | | 0.7615 | 33.94 | 22500 | 0.8013 | 0.6064 | 0.4565 | 0.5074 | | 0.7611 | 34.69 | 23000 | 0.8017 | 0.607 | 0.4567 | 0.5078 | | 0.7611 | 35.44 | 23500 | 0.8013 | 0.608 | 0.4565 | 0.5082 | | 0.7604 | 36.2 | 24000 | 0.8012 | 0.6069 | 0.4561 | 0.5072 | | 0.7599 | 36.95 | 24500 | 0.8013 | 0.6078 | 0.4571 | 0.5085 | | 0.7542 | 37.71 | 25000 | 0.8016 | 0.6083 | 0.4579 | 0.5091 | | 0.7637 | 38.46 | 25500 | 0.8009 | 0.6072 | 0.4569 | 0.5081 | | 0.7596 | 39.22 | 26000 | 0.8008 | 0.6069 | 0.4566 | 0.5078 | | 0.7604 | 39.97 | 26500 | 0.8008 | 0.6071 | 0.4566 | 0.5079 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-xxlarge-spanish-finetuned-mldoc

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

26

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--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-paraphrase-pubmed results: [] ---  # t5-small-paraphrase-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4032 - Rouge2 Precision: 0.8281 - Rouge2 Recall: 0.6346 - Rouge2 Fmeasure: 0.6996 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.5253 | 1.0 | 663 | 0.4895 | 0.8217 | 0.6309 | 0.695 | | 0.5385 | 2.0 | 1326 | 0.4719 | 0.822 | 0.6307 | 0.6953 | | 0.5255 | 3.0 | 1989 | 0.4579 | 0.8225 | 0.631 | 0.6954 | | 0.4927 | 4.0 | 2652 | 0.4510 | 0.824 | 0.6315 | 0.6965 | | 0.484 | 5.0 | 3315 | 0.4426 | 0.8254 | 0.6323 | 0.6974 | | 0.4691 | 6.0 | 3978 | 0.4383 | 0.8241 | 0.6311 | 0.6962 | | 0.4546 | 7.0 | 4641 | 0.4319 | 0.8248 | 0.6322 | 0.6969 | | 0.4431 | 8.0 | 5304 | 0.4270 | 0.8254 | 0.633 | 0.6977 | | 0.4548 | 9.0 | 5967 | 0.4257 | 0.8257 | 0.6322 | 0.6976 | | 0.4335 | 10.0 | 6630 | 0.4241 | 0.8271 | 0.6333 | 0.6986 | | 0.4234 | 11.0 | 7293 | 0.4203 | 0.827 | 0.6341 | 0.6992 | | 0.433 | 12.0 | 7956 | 0.4185 | 0.8279 | 0.6347 | 0.6998 | | 0.4108 | 13.0 | 8619 | 0.4161 | 0.8285 | 0.6352 | 0.7004 | | 0.4101 | 14.0 | 9282 | 0.4133 | 0.8289 | 0.6356 | 0.7008 | | 0.4155 | 15.0 | 9945 | 0.4149 | 0.8279 | 0.635 | 0.6998 | | 0.3991 | 16.0 | 10608 | 0.4124 | 0.8289 | 0.6353 | 0.7005 | | 0.3962 | 17.0 | 11271 | 0.4113 | 0.829 | 0.6353 | 0.7006 | | 0.3968 | 18.0 | 11934 | 0.4114 | 0.8285 | 0.6352 | 0.7002 | | 0.3962 | 19.0 | 12597 | 0.4100 | 0.8282 | 0.6346 | 0.6998 | | 0.3771 | 20.0 | 13260 | 0.4078 | 0.829 | 0.6352 | 0.7005 | | 0.3902 | 21.0 | 13923 | 0.4083 | 0.8295 | 0.6351 | 0.7006 | | 0.3811 | 22.0 | 14586 | 0.4077 | 0.8276 | 0.6346 | 0.6995 | | 0.38 | 23.0 | 15249 | 0.4076 | 0.8281 | 0.6346 | 0.6997 | | 0.3695 | 24.0 | 15912 | 0.4059 | 0.8277 | 0.6344 | 0.6993 | | 0.3665 | 25.0 | 16575 | 0.4043 | 0.8278 | 0.6343 | 0.6992 | | 0.3728 | 26.0 | 17238 | 0.4059 | 0.8279 | 0.6346 | 0.6994 | | 0.3669 | 27.0 | 17901 | 0.4048 | 0.8271 | 0.6342 | 0.6991 | | 0.3702 | 28.0 | 18564 | 0.4058 | 0.8265 | 0.6338 | 0.6985 | | 0.3674 | 29.0 | 19227 | 0.4049 | 0.8277 | 0.6345 | 0.6993 | | 0.364 | 30.0 | 19890 | 0.4048 | 0.8273 | 0.6341 | 0.699 | | 0.3618 | 31.0 | 20553 | 0.4041 | 0.828 | 0.6349 | 0.6997 | | 0.3609 | 32.0 | 21216 | 0.4040 | 0.8275 | 0.6346 | 0.6994 | | 0.357 | 33.0 | 21879 | 0.4037 | 0.8278 | 0.6348 | 0.6996 | | 0.3638 | 34.0 | 22542 | 0.4038 | 0.8275 | 0.634 | 0.6989 | | 0.3551 | 35.0 | 23205 | 0.4035 | 0.8275 | 0.6344 | 0.6992 | | 0.358 | 36.0 | 23868 | 0.4035 | 0.8279 | 0.6347 | 0.6995 | | 0.3519 | 37.0 | 24531 | 0.4034 | 0.8277 | 0.6343 | 0.6992 | | 0.359 | 38.0 | 25194 | 0.4035 | 0.8281 | 0.6346 | 0.6996 | | 0.3542 | 39.0 | 25857 | 0.4033 | 0.8281 | 0.6346 | 0.6996 | | 0.3592 | 40.0 | 26520 | 0.4032 | 0.8281 | 0.6346 | 0.6996 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

dccuchile/albert-xxlarge-spanish-finetuned-ner

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

28

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--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.923 - name: F1 type: f1 value: 0.9233262687967644 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2180 - Accuracy: 0.923 - F1: 0.9233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8217 | 1.0 | 250 | 0.3137 | 0.903 | 0.8999 | | 0.2484 | 2.0 | 500 | 0.2180 | 0.923 | 0.9233 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

dccuchile/albert-xxlarge-spanish-finetuned-pawsx

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

26

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--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - gborn/autonlp-data-news-summarization co2_eq_emissions: 210.6348731063569 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 483413089 - CO2 Emissions (in grams): 210.6348731063569 ## Validation Metrics - Loss: 1.8478657007217407 - Rouge1: 50.5981 - Rouge2: 26.2167 - RougeL: 46.0513 - RougeLsum: 46.061 - Gen Len: 13.5987 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/gborn/autonlp-news-summarization-483413089 ```

dccuchile/albert-xxlarge-spanish-finetuned-pos

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

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--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-cased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5956649094312695 ---  # bert-base-cased-finetuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6747 - Matthews Correlation: 0.5957 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name cola \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-cola \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4921 | 1.0 | 535 | 0.5283 | 0.5068 | | 0.2837 | 2.0 | 1070 | 0.5133 | 0.5521 | | 0.1775 | 3.0 | 1605 | 0.6747 | 0.5957 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/albert-xxlarge-spanish-finetuned-qa-mlqa

[ "pytorch", "albert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "AlbertForQuestionAnswering" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

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--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-mnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.8410292921074044 ---  # bert-base-cased-finetuned-mnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5721 - Accuracy: 0.8410 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-mnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5323 | 1.0 | 24544 | 0.4431 | 0.8302 | | 0.3447 | 2.0 | 49088 | 0.4725 | 0.8353 | | 0.2267 | 3.0 | 73632 | 0.5887 | 0.8368 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/albert-xxlarge-spanish-finetuned-xnli

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

68

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-cased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8602941176470589 - name: F1 type: f1 value: 0.9025641025641027 ---  # bert-base-cased-finetuned-mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.7132 - Accuracy: 0.8603 - F1: 0.9026 - Combined Score: 0.8814 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.5981 | 1.0 | 230 | 0.4580 | 0.7892 | 0.8562 | 0.8227 | | 0.3739 | 2.0 | 460 | 0.3806 | 0.8480 | 0.8942 | 0.8711 | | 0.1991 | 3.0 | 690 | 0.4879 | 0.8529 | 0.8958 | 0.8744 | | 0.1286 | 4.0 | 920 | 0.6342 | 0.8529 | 0.8986 | 0.8758 | | 0.0812 | 5.0 | 1150 | 0.7132 | 0.8603 | 0.9026 | 0.8814 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/albert-base-spanish

[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]

null

{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

586

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--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.9099395936298736 ---  # bert-base-cased-finetuned-qnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3986 - Accuracy: 0.9099 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.337 | 1.0 | 6547 | 0.9013 | 0.2448 | | 0.1971 | 2.0 | 13094 | 0.9143 | 0.2839 | | 0.1175 | 3.0 | 19641 | 0.9099 | 0.3986 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/albert-large-spanish

[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]

null

{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

75

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-base-cased-finetuned-qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue args: qqp metrics: - name: Accuracy type: accuracy value: 0.9083848627256987 - name: F1 type: f1 value: 0.8767633750332712 ---  # bert-base-cased-finetuned-qqp This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.3752 - Accuracy: 0.9084 - F1: 0.8768 - Combined Score: 0.8926 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.308 | 1.0 | 22741 | 0.2548 | 0.8925 | 0.8556 | 0.8740 | | 0.201 | 2.0 | 45482 | 0.2881 | 0.9032 | 0.8698 | 0.8865 | | 0.1416 | 3.0 | 68223 | 0.3752 | 0.9084 | 0.8768 | 0.8926 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/albert-tiny-spanish

[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]

null

{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

393

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6714801444043321 ---  # bert-base-cased-finetuned-rte This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7260 - Accuracy: 0.6715 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6915 | 1.0 | 156 | 0.6491 | 0.6606 | | 0.55 | 2.0 | 312 | 0.6737 | 0.6570 | | 0.3955 | 3.0 | 468 | 0.7260 | 0.6715 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/albert-xlarge-spanish

[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]

null

{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

91

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9231651376146789 ---  # bert-base-cased-finetuned-sst2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3649 - Accuracy: 0.9232 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name sst2 \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-sst2 \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.233 | 1.0 | 4210 | 0.9174 | 0.2841 | | 0.1261 | 2.0 | 8420 | 0.9278 | 0.3310 | | 0.0768 | 3.0 | 12630 | 0.9232 | 0.3649 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/albert-xxlarge-spanish

[ "pytorch", "tf", "albert", "pretraining", "es", "dataset:large_spanish_corpus", "transformers", "spanish", "OpenCENIA" ]

null

{ "architectures": [ "AlbertForPreTraining" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

42

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - spearmanr model-index: - name: bert-base-cased-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8897907271421561 ---  # bert-base-cased-finetuned-stsb This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4861 - Pearson: 0.8926 - Spearmanr: 0.8898 - Combined Score: 0.8912 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Combined Score | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:--------------:|:---------------:|:-------:|:---------:| | 1.1174 | 1.0 | 360 | 0.8816 | 0.5000 | 0.8832 | 0.8800 | | 0.3835 | 2.0 | 720 | 0.8901 | 0.4672 | 0.8915 | 0.8888 | | 0.2388 | 3.0 | 1080 | 0.8912 | 0.4861 | 0.8926 | 0.8898 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-cased-finetuned-mldoc

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

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27

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: bert-base-cased-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.4647887323943662 ---  # bert-base-cased-finetuned-wnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6996 - Accuracy: 0.4648 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name wnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-wnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7299 | 1.0 | 40 | 0.6923 | 0.5634 | | 0.6982 | 2.0 | 80 | 0.7027 | 0.3803 | | 0.6972 | 3.0 | 120 | 0.7005 | 0.4507 | | 0.6992 | 4.0 | 160 | 0.6977 | 0.5352 | | 0.699 | 5.0 | 200 | 0.6996 | 0.4648 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-cased-finetuned-ner

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

81

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-large-cased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5957317644481708 ---  # bert-large-cased-finetuned-cola This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.8385 - Matthews Correlation: 0.5957 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5533 | 1.0 | 2138 | 0.7943 | 0.4439 | | 0.5004 | 2.0 | 4276 | 0.7272 | 0.5678 | | 0.2865 | 3.0 | 6414 | 0.8385 | 0.5957 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-cased-finetuned-pawsx

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

25

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: bert-large-cased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.6838235294117647 - name: F1 type: f1 value: 0.8122270742358079 ---  # bert-large-cased-finetuned-mrpc This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6274 - Accuracy: 0.6838 - F1: 0.8122 - Combined Score: 0.7480 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6441 | 1.0 | 917 | 0.6370 | 0.6838 | 0.8122 | 0.7480 | | 0.6451 | 2.0 | 1834 | 0.6553 | 0.6838 | 0.8122 | 0.7480 | | 0.6428 | 3.0 | 2751 | 0.6332 | 0.6838 | 0.8122 | 0.7480 | | 0.6476 | 4.0 | 3668 | 0.6248 | 0.6838 | 0.8122 | 0.7480 | | 0.6499 | 5.0 | 4585 | 0.6274 | 0.6838 | 0.8122 | 0.7480 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-cased-finetuned-pos

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-large-cased-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6642599277978339 ---  # bert-large-cased-finetuned-rte This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 1.5187 - Accuracy: 0.6643 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6969 | 1.0 | 623 | 0.7039 | 0.5343 | | 0.5903 | 2.0 | 1246 | 0.6461 | 0.7184 | | 0.4557 | 3.0 | 1869 | 1.5187 | 0.6643 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa

[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

"2021-09-23T04:24:07Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: bert-large-cased-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.352112676056338 ---  # bert-large-cased-finetuned-wnli This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.7087 - Accuracy: 0.3521 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.7114 | 1.0 | 159 | 0.5634 | 0.6923 | | 0.7141 | 2.0 | 318 | 0.5634 | 0.6895 | | 0.7063 | 3.0 | 477 | 0.5634 | 0.6930 | | 0.712 | 4.0 | 636 | 0.4507 | 0.7077 | | 0.7037 | 5.0 | 795 | 0.3521 | 0.7087 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

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28

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-base-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.35940659235571387 ---  # fnet-base-finetuned-cola This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.5929 - Matthews Correlation: 0.3594 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name cola \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-cola \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5895 | 1.0 | 535 | 0.6146 | 0.1699 | | 0.4656 | 2.0 | 1070 | 0.5667 | 0.3047 | | 0.3329 | 3.0 | 1605 | 0.5929 | 0.3594 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

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39

"2021-09-17T07:11:04Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-mnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.7674938974776241 ---  # fnet-base-finetuned-mnli This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6443 - Accuracy: 0.7675 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-mnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7143 | 1.0 | 24544 | 0.6169 | 0.7504 | | 0.5407 | 2.0 | 49088 | 0.6218 | 0.7627 | | 0.4178 | 3.0 | 73632 | 0.6564 | 0.7658 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

"2021-09-16T17:30:22Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-base-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.7720588235294118 - name: F1 type: f1 value: 0.8502415458937198 ---  # fnet-base-finetuned-mrpc This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.9653 - Accuracy: 0.7721 - F1: 0.8502 - Combined Score: 0.8112 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.544 | 1.0 | 230 | 0.5272 | 0.7328 | 0.8300 | 0.7814 | | 0.4034 | 2.0 | 460 | 0.6211 | 0.7255 | 0.8298 | 0.7776 | | 0.2602 | 3.0 | 690 | 0.9110 | 0.7230 | 0.8306 | 0.7768 | | 0.1688 | 4.0 | 920 | 0.8640 | 0.7696 | 0.8489 | 0.8092 | | 0.0913 | 5.0 | 1150 | 0.9653 | 0.7721 | 0.8502 | 0.8112 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-pawsx

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

24

"2021-09-17T18:09:22Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-qnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE QNLI type: glue args: qnli metrics: - name: Accuracy type: accuracy value: 0.8438586857038257 ---  # fnet-base-finetuned-qnli This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4746 - Accuracy: 0.8439 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4597 | 1.0 | 6547 | 0.3713 | 0.8411 | | 0.3252 | 2.0 | 13094 | 0.3781 | 0.8420 | | 0.2243 | 3.0 | 19641 | 0.4746 | 0.8439 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-pos

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

"2021-09-18T18:23:31Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-base-finetuned-qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue args: qqp metrics: - name: Accuracy type: accuracy value: 0.8847390551570616 - name: F1 type: f1 value: 0.8466197090382463 ---  # fnet-base-finetuned-qqp This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.3686 - Accuracy: 0.8847 - F1: 0.8466 - Combined Score: 0.8657 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name qqp \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-qqp \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.3484 | 1.0 | 22741 | 0.3014 | 0.8676 | 0.8297 | 0.8487 | | 0.2387 | 2.0 | 45482 | 0.3011 | 0.8801 | 0.8429 | 0.8615 | | 0.1739 | 3.0 | 68223 | 0.3686 | 0.8847 | 0.8466 | 0.8657 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-qa-mlqa

[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "BertForQuestionAnswering" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

"2021-09-19T05:47:14Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.628158844765343 ---  # fnet-base-finetuned-rte This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6978 - Accuracy: 0.6282 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name rte \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-rte \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6829 | 1.0 | 156 | 0.6657 | 0.5704 | | 0.6174 | 2.0 | 312 | 0.6784 | 0.6101 | | 0.5141 | 3.0 | 468 | 0.6978 | 0.6282 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-xnli

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

36

"2021-09-19T08:32:11Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8944954128440367 ---  # fnet-base-finetuned-sst2 This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4674 - Accuracy: 0.8945 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name sst2 \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-sst2 \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.2956 | 1.0 | 4210 | 0.8819 | 0.3128 | | 0.1746 | 2.0 | 8420 | 0.8979 | 0.3850 | | 0.1204 | 3.0 | 12630 | 0.8945 | 0.4674 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/distilbert-base-spanish-uncased-finetuned-mldoc

[ "pytorch", "distilbert", "text-classification", "transformers" ]

text-classification

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27

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - spearmanr model-index: - name: fnet-base-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8219397497728022 ---  # fnet-base-finetuned-stsb This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.7894 - Pearson: 0.8256 - Spearmanr: 0.8219 - Combined Score: 0.8238 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir fnet-base-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Combined Score | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:--------------:|:---------------:|:-------:|:---------:| | 1.5473 | 1.0 | 360 | 0.8120 | 0.7751 | 0.8115 | 0.8125 | | 0.6954 | 2.0 | 720 | 0.8145 | 0.8717 | 0.8160 | 0.8130 | | 0.4828 | 3.0 | 1080 | 0.8238 | 0.7894 | 0.8256 | 0.8219 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/distilbert-base-spanish-uncased-finetuned-ner

[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

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28

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer - fnet-bert-base-comparison datasets: - glue metrics: - accuracy model-index: - name: fnet-base-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.5492957746478874 ---  # fnet-base-finetuned-wnli This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6887 - Accuracy: 0.5493 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path google/fnet-base \\n --task_name wnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir fnet-base-finetuned-wnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7052 | 1.0 | 40 | 0.6902 | 0.5634 | | 0.6957 | 2.0 | 80 | 0.7013 | 0.4366 | | 0.6898 | 3.0 | 120 | 0.6898 | 0.5352 | | 0.6958 | 4.0 | 160 | 0.6874 | 0.5634 | | 0.6982 | 5.0 | 200 | 0.6887 | 0.5493 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/distilbert-base-spanish-uncased-finetuned-pawsx

[ "pytorch", "distilbert", "text-classification", "transformers" ]

text-classification

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29

"2021-10-09T18:55:55Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 ---  # fnet-large-finetuned-cola-copy This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6243 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6195 | 1.0 | 2138 | 0.6527 | 0.0 | | 0.6168 | 2.0 | 4276 | 0.6259 | 0.0 | | 0.616 | 3.0 | 6414 | 0.6243 | 0.0 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/distilbert-base-spanish-uncased-finetuned-pos

[ "pytorch", "distilbert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "DistilBertForTokenClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

"2021-10-10T05:51:58Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 ---  # fnet-large-finetuned-cola-copy2 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6173 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6192 | 1.0 | 2138 | 0.6443 | 0.0 | | 0.6177 | 2.0 | 4276 | 0.6296 | 0.0 | | 0.6128 | 3.0 | 6414 | 0.6173 | 0.0 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/distilbert-base-spanish-uncased-finetuned-qa-mlqa

[ "pytorch", "distilbert", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "DistilBertForQuestionAnswering" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy3 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 ---  # fnet-large-finetuned-cola-copy3 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6554 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6408 | 1.0 | 2138 | 0.7329 | 0.0 | | 0.6589 | 2.0 | 4276 | 0.6311 | 0.0 | | 0.6467 | 3.0 | 6414 | 0.6554 | 0.0 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

dccuchile/distilbert-base-spanish-uncased-finetuned-xnli

[ "pytorch", "distilbert", "text-classification", "transformers" ]

text-classification

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31

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola-copy4 results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 ---  # fnet-large-finetuned-cola-copy4 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6500 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6345 | 1.0 | 2138 | 0.6611 | 0.0 | | 0.6359 | 2.0 | 4276 | 0.6840 | 0.0 | | 0.6331 | 3.0 | 6414 | 0.6500 | 0.0 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

CennetOguz/distilbert-base-uncased-finetuned-recipe-1

[ "pytorch", "tensorboard", "distilbert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

"2021-09-23T07:49:09Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: fnet-large-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: GLUE COLA type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.0 ---  # fnet-large-finetuned-cola This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6243 - Matthews Correlation: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6195 | 1.0 | 2138 | 0.6527 | 0.0 | | 0.6168 | 2.0 | 4276 | 0.6259 | 0.0 | | 0.616 | 3.0 | 6414 | 0.6243 | 0.0 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate

[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-large-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: GLUE MRPC type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8259803921568627 - name: F1 type: f1 value: 0.8798646362098139 ---  # fnet-large-finetuned-mrpc This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 1.0872 - Accuracy: 0.8260 - F1: 0.8799 - Combined Score: 0.8529 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.5656 | 1.0 | 917 | 0.6999 | 0.7843 | 0.8581 | 0.8212 | | 0.3874 | 2.0 | 1834 | 0.7280 | 0.8088 | 0.8691 | 0.8390 | | 0.1627 | 3.0 | 2751 | 1.1274 | 0.8162 | 0.8780 | 0.8471 | | 0.0751 | 4.0 | 3668 | 1.0289 | 0.8333 | 0.8870 | 0.8602 | | 0.0339 | 5.0 | 4585 | 1.0872 | 0.8260 | 0.8799 | 0.8529 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

Certified-Zoomer/DialoGPT-small-rick

[]

null

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0

"2021-10-09T08:47:27Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: fnet-large-finetuned-qqp results: - task: name: Text Classification type: text-classification dataset: name: GLUE QQP type: glue args: qqp metrics: - name: Accuracy type: accuracy value: 0.8943111550828593 - name: F1 type: f1 value: 0.8556565212985171 ---  # fnet-large-finetuned-qqp This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.5515 - Accuracy: 0.8943 - F1: 0.8557 - Combined Score: 0.8750 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| | 0.4574 | 1.0 | 90962 | 0.4946 | 0.8694 | 0.8297 | 0.8496 | | 0.3387 | 2.0 | 181924 | 0.4745 | 0.8874 | 0.8437 | 0.8655 | | 0.2029 | 3.0 | 272886 | 0.5515 | 0.8943 | 0.8557 | 0.8750 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

Chaddmckay/Cdm

[]

null

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0

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: fnet-large-finetuned-rte results: - task: name: Text Classification type: text-classification dataset: name: GLUE RTE type: glue args: rte metrics: - name: Accuracy type: accuracy value: 0.6425992779783394 ---  # fnet-large-finetuned-rte This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.7528 - Accuracy: 0.6426 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7105 | 1.0 | 623 | 0.6887 | 0.5740 | | 0.6714 | 2.0 | 1246 | 0.6742 | 0.6209 | | 0.509 | 3.0 | 1869 | 0.7528 | 0.6426 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

Chae/botman

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

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5

null

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: fnet-large-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9048165137614679 ---  # fnet-large-finetuned-sst2 This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.5240 - Accuracy: 0.9048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.394 | 1.0 | 16838 | 0.3896 | 0.8968 | | 0.2076 | 2.0 | 33676 | 0.5100 | 0.8956 | | 0.1148 | 3.0 | 50514 | 0.5240 | 0.9048 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

Chaewon/mmnt_decoder_en

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

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12

"2021-10-07T16:55:55Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: fnet-large-finetuned-stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8532669137129205 ---  # fnet-large-finetuned-stsb This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.6250 - Pearson: 0.8554 - Spearmanr: 0.8533 - Combined Score: 0.8543 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 1.0727 | 1.0 | 1438 | 0.7718 | 0.8187 | 0.8240 | 0.8214 | | 0.4619 | 2.0 | 2876 | 0.7704 | 0.8472 | 0.8500 | 0.8486 | | 0.2401 | 3.0 | 4314 | 0.6250 | 0.8554 | 0.8533 | 0.8543 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

Chaewon/mnmt_decoder_en

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

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8

"2021-09-23T05:28:41Z"

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: fnet-large-finetuned-wnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE WNLI type: glue args: wnli metrics: - name: Accuracy type: accuracy value: 0.38028169014084506 ---  # fnet-large-finetuned-wnli This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6953 - Accuracy: 0.3803 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7217 | 1.0 | 159 | 0.6864 | 0.5634 | | 0.7056 | 2.0 | 318 | 0.6869 | 0.5634 | | 0.706 | 3.0 | 477 | 0.6875 | 0.5634 | | 0.7032 | 4.0 | 636 | 0.6931 | 0.5634 | | 0.7025 | 5.0 | 795 | 0.6953 | 0.3803 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

chainyo/speaker-recognition-meetup

[]

null

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1

"2021-03-26T16:44:09Z"

--- language: cnh datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Hakha Chin by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice cnh type: common_voice args: cnh metrics: - name: Test WER type: wer value: 31.38 --- # Wav2Vec2-Large-XLSR-53-Hakha-Chin Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hakha Chin using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "cnh", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh/") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "cnh", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-cnh") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\/]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 31.38 % ## Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1pejk9gv9vMcUOjyVQ_vsV2ngW4NiWLWy?usp=sharing).

ChaitanyaU/FineTuneLM

[]

null

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0

"2021-03-27T16:56:10Z"

--- language: eo datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Esperanto by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice eo type: common_voice args: eo metrics: - name: Test WER type: wer value: 10.13 --- # Wav2Vec2-Large-XLSR-53-Esperanto Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "eo", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import jiwer def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(targets): chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end]) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] start += chunk_size end += chunk_size return float(S + D + I) / float(H + S + D) test_dataset = load_dataset("common_voice", "eo", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-eo') model.to("cuda") chars_to_ignore_regex = """[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�\\\\\\\\„\\\\\\\\«\\\\\\\$\\\\\\\\»\\\\\\\$\\\\\\\\’\\\\\\\\']""" resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace('—',' ').replace('–',' ') speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=5000))) ``` **Test Result**: 10.13 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-esperanto-asr-with-transformers-final.ipynb).

Chakita/Friends

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

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8

null

--- language: gu datasets: - openslr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Gujarati by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR gu type: openslr metrics: - name: Test WER type: wer value: 23.55 --- # Wav2Vec2-Large-XLSR-53-Gujarati Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Gujarati using the [OpenSLR SLR78](http://openslr.org/78/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Gujarati `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. # For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input. # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset_eval = test_dataset_eval.map(speech_file_to_array_fn) inputs = processor(test_dataset_eval["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset_eval["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-gu") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…\'\_\’]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 23.55 % ## Training 90% of the OpenSLR Gujarati Male+Female dataset was used for training, after removing few examples that contained Roman characters. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1fRQlgl4EPR4qKGScgza3MpWgbL5BeWtn?usp=sharing).

Chakita/KNUBert

[ "pytorch", "tensorboard", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

20

"2021-03-24T19:47:37Z"

--- language: hu datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Hungarian by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice hu type: common_voice args: hu metrics: - name: Test WER type: wer value: 46.75 --- # Wav2Vec2-Large-XLSR-53-Hungarian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hungarian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "hu", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "hu", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-hu") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 46.75 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-hungarian-asr.ipynb). The notebook containing the code used for evaluation can be found [here](https://colab.research.google.com/drive/1esYvWS6IkTQFfRqi_b6lAJEycuecInHE?usp=sharing).

Chakita/KROBERT

[ "pytorch", "roberta", "fill-mask", "transformers", "masked-lm", "fill-in-the-blanks", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- language: ia datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Interlingua by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice ia type: common_voice args: ia metrics: - name: Test WER type: wer value: 25.09 --- # Wav2Vec2-Large-XLSR-53-Interlingua Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Interlingua using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ia", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Odia test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ia", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-ia") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 25.09 % ## Training The Common Voice `train` and `validation` datasets were used for training for 4000 steps due to GPU timeout. The results are based on the 4000 steps checkpoint. There is a good chance that full training will lead to better results. The colab notebook used can be found [here](https://colab.research.google.com/drive/1nbqvVwS8DTNrCzzh3vgrN55qxgoqbita?usp=sharing) and the evaluation can be found [here](https://colab.research.google.com/drive/18pCWBwNNUMUYV1FiqT_0EsTbCfwwe7ms?usp=sharing).

Chakita/Kalbert

[ "pytorch", "tensorboard", "albert", "fill-mask", "transformers", "generated_from_trainer", "license:mit", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

null

--- language: it datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Italian by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice it type: common_voice args: it metrics: - name: Test WER type: wer value: 11.49 --- # Wav2Vec2-Large-XLSR-53-Italian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Italian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "it", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import unicodedata import jiwer def chunked_wer(targets, predictions, chunk_size=None): if chunk_size is None: return jiwer.wer(targets, predictions) start = 0 end = chunk_size H, S, D, I = 0, 0, 0, 0 while start < len(targets): chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end]) H = H + chunk_metrics["hits"] S = S + chunk_metrics["substitutions"] D = D + chunk_metrics["deletions"] I = I + chunk_metrics["insertions"] start += chunk_size end += chunk_size return float(S + D + I) / float(H + S + D) allowed_characters = [ " ", "'", 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'à', 'á', 'è', 'é', 'ì', 'í', 'ò', 'ó', 'ù', 'ú', ] def remove_accents(input_str): if input_str in allowed_characters: return input_str if input_str == 'ø': return 'o' elif input_str=='ß' or input_str =='ß': return 'b' elif input_str=='ё': return 'e' elif input_str=='đ': return 'd' nfkd_form = unicodedata.normalize('NFKD', input_str) only_ascii = nfkd_form.encode('ASCII', 'ignore').decode() if only_ascii is None or only_ascii=='': return input_str else: return only_ascii def fix_accents(sentence): new_sentence='' for char in sentence: new_sentence+=remove_accents(char) return new_sentence test_dataset = load_dataset("common_voice", "it", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') model = Wav2Vec2ForCTC.from_pretrained('gchhablani/wav2vec2-large-xlsr-it') model.to("cuda") resampler = torchaudio.transforms.Resample(48_000, 16_000) chars_to_remove= [",", "?", ".", "!", "-", ";", ":", '""', "%", '"', "�",'ʿ','“','”','(','=','`','_','+','«','<','>','~','…','«','»','–','\[','\]','°','̇','´','ʾ','„','̇','̇','̇','¡'] # All extra characters chars_to_remove_regex = f'[{"".join(chars_to_remove)}]' # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower().replace('‘',"'").replace('ʻ',"'").replace('ʼ',"'").replace('’',"'").replace('ʹ',"''").replace('̇','') batch["sentence"] = fix_accents(batch["sentence"]) speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=5000))) ``` **Test Result**: 11.49 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://github.com/gchhablani/wav2vec2-week/blob/main/fine-tune-xlsr-wav2vec2-on-italian-asr-with-transformers_final.ipynb).

Chakita/KannadaBERT

[ "pytorch", "roberta", "fill-mask", "transformers", "masked-lm", "fill-in-the-blanks", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "RobertaForMaskedLM" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

"2021-03-25T20:55:44Z"

--- language: mr datasets: - interspeech_2021_asr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Marathi 2 by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: InterSpeech 2021 ASR mr type: interspeech_2021_asr metrics: - name: Test WER type: wer value: 14.53 --- # Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using a part of the [InterSpeech 2021 Marathi](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") resampler = torchaudio.transforms.Resample(8_000, 16_000) # The original data was with 8,000 sampling rate. You can change it according to your input. # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the test set of the Marathi data on InterSpeech-2021. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-2") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]' resampler = torchaudio.transforms.Resample(8_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 19.98 % (555 examples from test set were used for evaluation) **Test Result on 10% of OpenSLR74 data**: 64.64 % ## Training 5000 examples of the InterSpeech Marathi dataset were used for training. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1sIwGOLJPQqhKm_wVZDkzRuoJqAEgArFr?usp=sharing).

Chakita/gpt2_mwp

[ "pytorch", "gpt2", "text-generation", "transformers" ]

text-generation

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6

"2021-03-26T00:56:33Z"

--- language: mr datasets: - openslr - interspeech_2021_asr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR mr, InterSpeech 2021 ASR mr type: openslr, interspeech_2021_asr metrics: - name: Test WER type: wer value: 19.05 --- # Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset and [InterSpeech 2021](https://navana-tech.github.io/IS21SS-indicASRchallenge/data.html) Marathi datasets. Note that this data OpenSLR contains only female voices. Please keep this in mind before using the model for your task. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `text` and `audio_path` fields: ```python import torch import torchaudio import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["audio_path"]) batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) # sampling_rate can vary return batch test_data= test_data.map(speech_file_to_array_fn) inputs = processor(test_data["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_data["text"][:2]) ``` ## Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ```python import torch import torchaudio import librosa from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_data = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr-3") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]' # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower() speech_array, sampling_rate = torchaudio.load(batch["audio_path"]) batch["speech"] = librosa.resample(speech_array[0].numpy(), sampling_rate, 16_000) return batch test_data= test_data.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_data.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"]))) ``` **Test Result**: 19.05 % (157+157 examples) **Test Result on OpenSLR test**: 14.15 % (157 examples) **Test Results on InterSpeech test**: 27.14 % (157 examples) ## Training 1412 examples of the OpenSLR Marathi dataset and 1412 examples of InterSpeech 2021 Marathi ASR dataset were used for training. For testing, 157 examples from each were used. The colab notebook used for training and evaluation can be found [here](https://colab.research.google.com/drive/15fUhb4bUFFGJyNLr-_alvPxVX4w0YXRu?usp=sharing).

Chalponkey/DialoGPT-small-Barry

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

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11

null

--- language: mr datasets: - openslr metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Marathi by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: OpenSLR mr type: openslr metrics: - name: Test WER type: wer value: 14.53 --- # Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, although it works very well for male voice too. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Marathi `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input. # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on 10% of the Marathi data on OpenSLR. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re # test_dataset = #TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 14.53 % ## Training 90% of the OpenSLR Marathi dataset was used for training. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1_BbLyLqDUsXG3RpSULfLRjC6UY3RjwME?usp=sharing).

Champion/test_upload_vox2_wavlm_epoch8

[ "sidekit", "audio" ]

null

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0

null

--- language: or datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Large 53 Odia by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice or type: common_voice args: or metrics: - name: Test WER type: wer value: 52.64 --- # Wav2Vec2-Large-XLSR-53-Odia Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Odia using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "or", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Odia test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "or", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-or") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…\'\_\’\।\|]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 52.64 % ## Training The Common Voice `train` and `validation` datasets were used for training.The colab notebook used can be found [here](https://colab.research.google.com/drive/1s8DrwgB5y4Z7xXIrPXo1rQA5_1OZ8WD5?usp=sharing).

Chan/distilgpt2-finetuned-wikitext2

[]

null

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0

"2021-03-23T13:56:09Z"

--- language: pt datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Portugese by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice pt type: common_voice args: pt metrics: - name: Test WER type: wer value: 17.22 --- # Wav2Vec2-Large-XLSR-53-Portuguese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "pt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "pt", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\;\"\“\'\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 17.22 % ## Training The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://github.com/jqueguiner/wav2vec2-sprint/blob/main/run_common_voice.py). The parameters passed were: ```bash #!/usr/bin/env bash python run_common_voice.py \ --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \ --dataset_config_name="pt" \ --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \ --cache_dir=/workspace/data \ --overwrite_output_dir \ --num_train_epochs="30" \ --per_device_train_batch_size="32" \ --per_device_eval_batch_size="32" \ --evaluation_strategy="steps" \ --learning_rate="3e-4" \ --warmup_steps="500" \ --fp16 \ --freeze_feature_extractor \ --save_steps="500" \ --eval_steps="500" \ --save_total_limit="1" \ --logging_steps="500" \ --group_by_length \ --feat_proj_dropout="0.0" \ --layerdrop="0.1" \ --gradient_checkpointing \ --do_train --do_eval \ ``` Notebook containing the evaluation can be found [here](https://colab.research.google.com/drive/14e-zNK_5pm8EMY9EbeZerpHx7WsGycqG?usp=sharing).

Chan/distilroberta-base-finetuned-wikitext2

[]

null

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0

"2021-03-27T14:23:51Z"

--- language: rm-sursilv datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2 Large 53 Romansh Sursilvan by Gunjan Chhablani results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice rm-sursilv type: common_voice args: rm-sursilv metrics: - name: Test WER type: wer value: 25.16 --- # Wav2Vec2-Large-XLSR-53-Romansh-Sursilvan Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Romansh Sursilvan using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "rm-sursilv", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Portuguese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "rm-sursilv", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-rm-sursilv") model.to("cuda") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\…\\«\\»\\–]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 25.16 % ## Training The Common Voice `train` and `validation` datasets were used for training. The code can be found [here](https://colab.research.google.com/drive/1dpZr_GzRowCciUbzM3GnW04TNKnB7vrP?usp=sharing).

Cheapestmedsshop/Buymodafinilus

[]

null

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0

"2021-07-28T12:51:00Z"

--- language: el --- # GreekSocialBERT ## Model description A Greek language model based on [GreekBERT](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) ## Training data The training data is a corpus of 458,293 documents collected from Greek social media accounts. The training corpus has been collected and provided by [Palo LTD](http://www.paloservices.com/) ## Eval results ### BibTeX entry and citation info ```bibtex @Article{info12080331, AUTHOR = {Alexandridis, Georgios and Varlamis, Iraklis and Korovesis, Konstantinos and Caridakis, George and Tsantilas, Panagiotis}, TITLE = {A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media}, JOURNAL = {Information}, VOLUME = {12}, YEAR = {2021}, NUMBER = {8}, ARTICLE-NUMBER = {331}, URL = {https://www.mdpi.com/2078-2489/12/8/331}, ISSN = {2078-2489}, DOI = {10.3390/info12080331} } ```

Cheatham/xlm-roberta-base-finetuned

[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]

text-classification

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20

null

--- language: el --- # PaloBERT ## Model description A Greek language model based on [RoBERTa](https://arxiv.org/abs/1907.11692) ## Training data The training data is a corpus of 458,293 documents collected from Greek social media accounts. It also contains a GTP-2 tokenizer trained from scratch on the same corpus. The training corpus has been collected and provided by [Palo LTD](http://www.paloservices.com/) ## Eval results ### BibTeX entry and citation info ```bibtex @Article{info12080331, AUTHOR = {Alexandridis, Georgios and Varlamis, Iraklis and Korovesis, Konstantinos and Caridakis, George and Tsantilas, Panagiotis}, TITLE = {A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media}, JOURNAL = {Information}, VOLUME = {12}, YEAR = {2021}, NUMBER = {8}, ARTICLE-NUMBER = {331}, URL = {https://www.mdpi.com/2078-2489/12/8/331}, ISSN = {2078-2489}, DOI = {10.3390/info12080331} } ```

Check/vaw2tmp

[ "tensorboard" ]

null

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0

null

https://dl.fbaipublicfiles.com/avhubert/model/lrs3_vox/vsr/base_vox_433h.pt

D3xter1922/electra-base-discriminator-finetuned-mnli

[]

null

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0

null

A fake news detector using RoBERTa. Dataset: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset Training involved using hyperparameter search with 10 trials.

DJSammy/bert-base-danish-uncased_BotXO-ai

[ "pytorch", "jax", "da", "dataset:common_crawl", "dataset:wikipedia", "transformers", "bert", "masked-lm", "license:cc-by-4.0", "fill-mask" ]

fill-mask

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14

null

This repository belongs to TransportersBERT from ActTrans publication. Taju, Semmy Wellem, Syed Muazzam Ali Shah, and Yu-Yen Ou. “ActTRANS: Functional Classification in Active Transport Proteins Based on Transfer Learning and Contextual Representations.” Computational Biology and Chemistry 93 (August 1, 2021): 107537. https://doi.org/10.1016/j.compbiolchem.2021.107537.

DTAI-KULeuven/robbertje-1-gb-shuffled

[ "pytorch", "roberta", "fill-mask", "nl", "dataset:oscar", "dataset:oscar (NL)", "dataset:dbrd", "dataset:lassy-ud", "dataset:europarl-mono", "dataset:conll2002", "arxiv:2101.05716", "transformers", "Dutch", "Flemish", "RoBERTa", "RobBERT", "RobBERTje", "license:mit", "autotrain_compatible" ]

fill-mask

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7

null

--- language: bn tags: - text-generation widget: - text: তোমাকে দেখেছি আমার হৃদয় মাঝে --- # Robi Kobi ### Created by [Ritobrata Ghosh](https://ghosh-r.github.io) A model that writes Bengali poems in the style of Nobel Laureate poet Rabindranath Tagore. This model is fine-tuned on 1,400+ poems written by Rabindranath Tagore. This model leverages the [Bangla GPT-2](https://huggingface.co/ghosh-r/bangla-gpt2) pretrained model, trained on mc4-Bengali dataset.

Daiki/scibert_scivocab_uncased-finetuned-cola

[]

null

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0

null

--- language: - ro license: apache-2.0 tags: - automatic-speech-recognition - hf-asr-leaderboard - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 - gigant/romanian_speech_synthesis_0_8_1 model-index: - name: wav2vec2-ro-300m_01 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event type: speech-recognition-community-v2/dev_data args: ro metrics: - name: Dev WER (without LM) type: wer value: 46.99 - name: Dev CER (without LM) type: cer value: 16.04 - name: Dev WER (with LM) type: wer value: 38.63 - name: Dev CER (with LM) type: cer value: 14.52 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice type: mozilla-foundation/common_voice_8_0 args: ro metrics: - name: Test WER (without LM) type: wer value: 11.73 - name: Test CER (without LM) type: cer value: 2.93 - name: Test WER (with LM) type: wer value: 7.31 - name: Test CER (with LM) type: cer value: 2.17 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: ro metrics: - name: Test WER type: wer value: 43.23 --- You can test this model online with the [**Space for Romanian Speech Recognition**](https://huggingface.co/spaces/gigant/romanian-speech-recognition) The model ranked **TOP-1** on Romanian Speech Recognition during HuggingFace's Robust Speech Challenge : * [**The 🤗 Speech Bench**](https://huggingface.co/spaces/huggingface/hf-speech-bench) * [**Speech Challenge Leaderboard**](https://huggingface.co/spaces/speech-recognition-community-v2/FinalLeaderboard) # Romanian Wav2Vec2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) dataset, with extra training data from [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) dataset. Without the 5-gram Language Model optimization, it achieves the following results on the evaluation set (Common Voice 8.0, Romanian subset, test split): - Loss: 0.1553 - Wer: 0.1174 - Cer: 0.0294 ## Model description The architecture is based on [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) with a speech recognition CTC head and an added 5-gram language model (using [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) and [kenlm](https://github.com/kpu/kenlm)) trained on the [Romanian Corpora Parliament](gigant/ro_corpora_parliament_processed) dataset. Those libraries are needed in order for the language model-boosted decoder to work. ## Intended uses & limitations The model is made for speech recognition in Romanian from audio clips sampled at **16kHz**. The predicted text is lowercased and does not contain any punctuation. ## How to use Make sure you have installed the correct dependencies for the language model-boosted version to work. You can just run this command to install the `kenlm` and `pyctcdecode` libraries : ```pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode``` With the framework `transformers` you can load the model with the following code : ``` from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("gigant/romanian-wav2vec2") model = AutoModelForCTC.from_pretrained("gigant/romanian-wav2vec2") ``` Or, if you want to test the model, you can load the automatic speech recognition pipeline from `transformers` with : ``` from transformers import pipeline asr = pipeline("automatic-speech-recognition", model="gigant/romanian-wav2vec2") ``` ## Example use with the `datasets` library First, you need to load your data We will use the [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) dataset in this example. ``` from datasets import load_dataset dataset = load_dataset("gigant/romanian_speech_synthesis_0_8_1") ``` You can listen to the samples with the `IPython.display` library : ``` from IPython.display import Audio i = 0 sample = dataset["train"][i] Audio(sample["audio"]["array"], rate = sample["audio"]["sampling_rate"]) ``` The model is trained to work with audio sampled at 16kHz, so if the sampling rate of the audio in the dataset is different, we will have to resample it. In the example, the audio is sampled at 48kHz. We can see this by checking `dataset["train"][0]["audio"]["sampling_rate"]` The following code resample the audio using the `torchaudio` library : ``` import torchaudio import torch i = 0 audio = sample["audio"]["array"] rate = sample["audio"]["sampling_rate"] resampler = torchaudio.transforms.Resample(rate, 16_000) audio_16 = resampler(torch.Tensor(audio)).numpy() ``` To listen to the resampled sample : ``` Audio(audio_16, rate=16000) ``` Know you can get the model prediction by running ``` predicted_text = asr(audio_16) ground_truth = dataset["train"][i]["sentence"] print(f"Predicted text : {predicted_text}") print(f"Ground truth : {ground_truth}") ``` ## Training and evaluation data Training data : - [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : train + validation + other splits - [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) : train + test splits Evaluation data : - [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : test split ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 2.9272 | 0.78 | 500 | 0.7603 | 0.7734 | 0.2355 | | 0.6157 | 1.55 | 1000 | 0.4003 | 0.4866 | 0.1247 | | 0.4452 | 2.33 | 1500 | 0.2960 | 0.3689 | 0.0910 | | 0.3631 | 3.11 | 2000 | 0.2580 | 0.3205 | 0.0796 | | 0.3153 | 3.88 | 2500 | 0.2465 | 0.2977 | 0.0747 | | 0.2795 | 4.66 | 3000 | 0.2274 | 0.2789 | 0.0694 | | 0.2615 | 5.43 | 3500 | 0.2277 | 0.2685 | 0.0675 | | 0.2389 | 6.21 | 4000 | 0.2135 | 0.2518 | 0.0627 | | 0.2229 | 6.99 | 4500 | 0.2054 | 0.2449 | 0.0614 | | 0.2067 | 7.76 | 5000 | 0.2096 | 0.2378 | 0.0597 | | 0.1977 | 8.54 | 5500 | 0.2042 | 0.2387 | 0.0600 | | 0.1896 | 9.32 | 6000 | 0.2110 | 0.2383 | 0.0595 | | 0.1801 | 10.09 | 6500 | 0.1909 | 0.2165 | 0.0548 | | 0.174 | 10.87 | 7000 | 0.1883 | 0.2206 | 0.0559 | | 0.1685 | 11.65 | 7500 | 0.1848 | 0.2097 | 0.0528 | | 0.1591 | 12.42 | 8000 | 0.1851 | 0.2039 | 0.0514 | | 0.1537 | 13.2 | 8500 | 0.1881 | 0.2065 | 0.0518 | | 0.1504 | 13.97 | 9000 | 0.1840 | 0.1972 | 0.0499 | | 0.145 | 14.75 | 9500 | 0.1845 | 0.2029 | 0.0517 | | 0.1417 | 15.53 | 10000 | 0.1884 | 0.2003 | 0.0507 | | 0.1364 | 16.3 | 10500 | 0.2010 | 0.2037 | 0.0517 | | 0.1331 | 17.08 | 11000 | 0.1838 | 0.1923 | 0.0483 | | 0.129 | 17.86 | 11500 | 0.1818 | 0.1922 | 0.0489 | | 0.1198 | 18.63 | 12000 | 0.1760 | 0.1861 | 0.0465 | | 0.1203 | 19.41 | 12500 | 0.1686 | 0.1839 | 0.0465 | | 0.1225 | 20.19 | 13000 | 0.1828 | 0.1920 | 0.0479 | | 0.1145 | 20.96 | 13500 | 0.1673 | 0.1784 | 0.0446 | | 0.1053 | 21.74 | 14000 | 0.1802 | 0.1810 | 0.0456 | | 0.1071 | 22.51 | 14500 | 0.1769 | 0.1775 | 0.0444 | | 0.1053 | 23.29 | 15000 | 0.1920 | 0.1783 | 0.0457 | | 0.1024 | 24.07 | 15500 | 0.1904 | 0.1775 | 0.0446 | | 0.0987 | 24.84 | 16000 | 0.1793 | 0.1762 | 0.0446 | | 0.0949 | 25.62 | 16500 | 0.1801 | 0.1766 | 0.0443 | | 0.0942 | 26.4 | 17000 | 0.1731 | 0.1659 | 0.0423 | | 0.0906 | 27.17 | 17500 | 0.1776 | 0.1698 | 0.0424 | | 0.0861 | 27.95 | 18000 | 0.1716 | 0.1600 | 0.0406 | | 0.0851 | 28.73 | 18500 | 0.1662 | 0.1630 | 0.0410 | | 0.0844 | 29.5 | 19000 | 0.1671 | 0.1572 | 0.0393 | | 0.0792 | 30.28 | 19500 | 0.1768 | 0.1599 | 0.0407 | | 0.0798 | 31.06 | 20000 | 0.1732 | 0.1558 | 0.0394 | | 0.0779 | 31.83 | 20500 | 0.1694 | 0.1544 | 0.0388 | | 0.0718 | 32.61 | 21000 | 0.1709 | 0.1578 | 0.0399 | | 0.0732 | 33.38 | 21500 | 0.1697 | 0.1523 | 0.0391 | | 0.0708 | 34.16 | 22000 | 0.1616 | 0.1474 | 0.0375 | | 0.0678 | 34.94 | 22500 | 0.1698 | 0.1474 | 0.0375 | | 0.0642 | 35.71 | 23000 | 0.1681 | 0.1459 | 0.0369 | | 0.0661 | 36.49 | 23500 | 0.1612 | 0.1411 | 0.0357 | | 0.0629 | 37.27 | 24000 | 0.1662 | 0.1414 | 0.0355 | | 0.0587 | 38.04 | 24500 | 0.1659 | 0.1408 | 0.0351 | | 0.0581 | 38.82 | 25000 | 0.1612 | 0.1382 | 0.0352 | | 0.0556 | 39.6 | 25500 | 0.1647 | 0.1376 | 0.0345 | | 0.0543 | 40.37 | 26000 | 0.1658 | 0.1335 | 0.0337 | | 0.052 | 41.15 | 26500 | 0.1716 | 0.1369 | 0.0343 | | 0.0513 | 41.92 | 27000 | 0.1600 | 0.1317 | 0.0330 | | 0.0491 | 42.7 | 27500 | 0.1671 | 0.1311 | 0.0328 | | 0.0463 | 43.48 | 28000 | 0.1613 | 0.1289 | 0.0324 | | 0.0468 | 44.25 | 28500 | 0.1599 | 0.1260 | 0.0315 | | 0.0435 | 45.03 | 29000 | 0.1556 | 0.1232 | 0.0308 | | 0.043 | 45.81 | 29500 | 0.1588 | 0.1240 | 0.0309 | | 0.0421 | 46.58 | 30000 | 0.1567 | 0.1217 | 0.0308 | | 0.04 | 47.36 | 30500 | 0.1533 | 0.1198 | 0.0302 | | 0.0389 | 48.14 | 31000 | 0.1582 | 0.1185 | 0.0297 | | 0.0387 | 48.91 | 31500 | 0.1576 | 0.1187 | 0.0297 | | 0.0376 | 49.69 | 32000 | 0.1560 | 0.1182 | 0.0295 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.0 - pyctcdecode 0.3.0 - kenlm