modelId
stringlengths 4
81
| tags
sequence | pipeline_tag
stringclasses 17
values | config
dict | downloads
int64 0
59.7M
| first_commit
timestamp[ns, tz=UTC] | card
stringlengths 51
438k
|
|---|---|---|---|---|---|---|
Declan/NPR_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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
}
}
} | 3 | null | ---
license: cc-by-4.0
---
# BART-base fine-tuned on NaturalQuestions for **Question Generation**
[BART Model](https://arxiv.org/pdf/1910.13461.pdf) fine-tuned on [Google NaturalQuestions](https://ai.google.com/research/NaturalQuestions/) for **Question Generation** by treating long answer as input, and question as output.
## Details of BART
The **BART** model was presented in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by *Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, Luke Zettlemoyer* in Here the abstract:
We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.
## Details of the downstream task (QG) - Dataset 📚 🧐
Dataset: ```NaturalQuestions``` from Google (https://ai.google.com/research/NaturalQuestions/)
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| NaturalQuestions | train | 97650 |
| NaturalQuestions | valid | 10850 |
## Model fine-tuning 🏋️
The training script can be found [here](https://github.com/McGill-NLP/MLQuestions/blob/main/QG/train.py)
## Model in Action 🚀
```python
from transformers import AutoModel, BartTokenizer
#Load the tokenizer
tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
#Load the model
model = AutoModelForSeq2SeqLM.from_pretrained("McGill-NLP/bart-qg-nq-checkpoint")
```
## Citation
If you want to cite this model you can use this:
```bibtex
@inproceedings{kulshreshtha-etal-2021-back,
title = "Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval",
author = "Kulshreshtha, Devang and
Belfer, Robert and
Serban, Iulian Vlad and
Reddy, Siva",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.566",
pages = "7064--7078",
abstract = "In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6{\%} top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.",
}
```
> Created by [Devang Kulshreshtha](https://geekydevu.netlify.app/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
Declan/NewYorkPost_model_v1 | [] | 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 | Fatima Fellowship Quick Coding Challenge (Pick 1):
- Deep Learning for Vision
|
Declan/NewYorkTimes_model_v1 | [] | 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: mit
tags:
- generated_from_trainer
model-index:
- name: poem-gen-spanish-t5-small-d2
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. -->
# poem-gen-spanish-t5-small-d2
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9027
## 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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.223 | 0.73 | 30000 | 3.1479 |
| 3.0109 | 1.46 | 60000 | 3.0544 |
| 2.8649 | 2.19 | 90000 | 2.9730 |
| 2.7603 | 2.93 | 120000 | 2.9301 |
| 2.6343 | 3.66 | 150000 | 2.9188 |
| 2.5094 | 4.39 | 180000 | 2.9064 |
| 2.391 | 5.12 | 210000 | 2.9073 |
| 2.3592 | 5.85 | 240000 | 2.9022 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Declan/NewYorkTimes_model_v4 | [] | 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
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: canine-c-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: 0.0990441507705203
---
<!-- 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. -->
# canine-c-finetuned-cola
This model is a fine-tuned version of [google/canine-c](https://huggingface.co/google/canine-c) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6246
- Matthews Correlation: 0.0990
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6142 | 1.0 | 535 | 0.6268 | 0.0 |
| 0.607 | 2.0 | 1070 | 0.6234 | 0.0 |
| 0.6104 | 3.0 | 1605 | 0.6226 | 0.0 |
| 0.5725 | 4.0 | 2140 | 0.6246 | 0.0990 |
| 0.5426 | 5.0 | 2675 | 0.6866 | 0.0495 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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
}
}
} | 3 | 2022-04-01T17:59:52Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: juaner/distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# juaner/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1909
- Validation Loss: 0.5553
- Train Matthews Correlation: 0.5279
- Epoch: 2
## 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:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5191 | 0.4491 | 0.4718 | 0 |
| 0.3270 | 0.4571 | 0.5196 | 1 |
| 0.1909 | 0.5553 | 0.5279 | 2 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
|
Declan/Politico_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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
}
}
} | 9 | 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: 0.936
- name: F1
type: f1
value: 0.9361334972007946
---
<!-- 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: 0.2205
- Accuracy: 0.936
- F1: 0.9361
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0442 | 1.0 | 250 | 0.2392 | 0.926 | 0.9265 |
| 0.0463 | 2.0 | 500 | 0.2205 | 0.936 | 0.9361 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Declan/Politico_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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
}
}
} | 3 | null | pssteval INFO: ASR metrics for split `valid` FER: 9.8% PER: 20.9% |
Declan/Reuters_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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
}
}
} | 7 | null | ---
language: en
datasets:
- Crunchbase
---
# Company Classifier
This fine-tuned Distilbert model is using company descriptions for classification. The model is tasked to classify the company as either finance or biotech. The demo can be found on my profile under Spaces (https://huggingface.co/erikacardenas300).
I hope you enjoy it! |
Declan/Reuters_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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
}
}
} | 7 | null | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- abd-1999/autotrain-data-bbc-news-summarization
co2_eq_emissions: 2313.4037079026934
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 694821095
- CO2 Emissions (in grams): 2313.4037079026934
## Validation Metrics
- Loss: 3.0294156074523926
- Rouge1: 2.1467
- Rouge2: 0.0853
- RougeL: 2.1524
- RougeLsum: 2.1534
- Gen Len: 18.5603
## 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 AutoTrain"}' https://api-inference.huggingface.co/abd-1999/autotrain-bbc-news-summarization-694821095
``` |
Declan/Reuters_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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
}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 25.5784
---
<!-- 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-wikihow_3epoch
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5163
- Rouge1: 25.5784
- Rouge2: 8.9929
- Rougel: 21.5345
- Rougelsum: 24.9382
- Gen Len: 18.384
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.9421 | 0.25 | 5000 | 2.6545 | 23.2336 | 7.5502 | 19.5899 | 22.5521 | 18.4076 |
| 2.8411 | 0.51 | 10000 | 2.6103 | 24.3524 | 8.2068 | 20.5238 | 23.6679 | 18.2606 |
| 2.7983 | 0.76 | 15000 | 2.5836 | 24.8169 | 8.4826 | 20.8765 | 24.1686 | 18.3211 |
| 2.7743 | 1.02 | 20000 | 2.5627 | 24.9904 | 8.5625 | 21.0344 | 24.3416 | 18.3786 |
| 2.7452 | 1.27 | 25000 | 2.5508 | 25.1497 | 8.6872 | 21.152 | 24.4751 | 18.3524 |
| 2.7353 | 1.53 | 30000 | 2.5384 | 25.2909 | 8.7408 | 21.2344 | 24.629 | 18.4453 |
| 2.7261 | 1.78 | 35000 | 2.5322 | 25.3748 | 8.7802 | 21.312 | 24.7191 | 18.3754 |
| 2.7266 | 2.03 | 40000 | 2.5265 | 25.4095 | 8.8915 | 21.3871 | 24.7685 | 18.4013 |
| 2.706 | 2.29 | 45000 | 2.5211 | 25.4372 | 8.8926 | 21.4124 | 24.7902 | 18.3776 |
| 2.7073 | 2.54 | 50000 | 2.5176 | 25.4925 | 8.9668 | 21.5103 | 24.8608 | 18.4303 |
| 2.703 | 2.8 | 55000 | 2.5163 | 25.5784 | 8.9929 | 21.5345 | 24.9382 | 18.384 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
DeepChem/ChemBERTa-77M-MLM | [
"pytorch",
"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
}
}
} | 2,416 | 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: 0.926
- name: F1
type: f1
value: 0.9260113300845928
---
<!-- 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: 0.2280
- Accuracy: 0.926
- F1: 0.9260
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8646 | 1.0 | 250 | 0.3326 | 0.9045 | 0.9009 |
| 0.2663 | 2.0 | 500 | 0.2280 | 0.926 | 0.9260 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6
|
DeepPavlov/bert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 3,009 | null | ---
title: DualStyleGAN
emoji: 👀
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 2.8.13
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
DeltaHub/adapter_t5-3b_mrpc | [
"pytorch",
"transformers"
] | 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
}
}
} | 3 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/clortown/1648875085007/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488574779351187458/RlIQNUFG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">yeosang elf agenda</div>
<div style="text-align: center; font-size: 14px;">@clortown</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from yeosang elf agenda.
| Data | yeosang elf agenda |
| --- | --- |
| Tweets downloaded | 3140 |
| Retweets | 538 |
| Short tweets | 463 |
| Tweets kept | 2139 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cupnlna/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @clortown's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uii743r9) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uii743r9/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/clortown')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
DeltaHub/adapter_t5-3b_qnli | [
"pytorch",
"transformers"
] | 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
}
}
} | 3 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: vliegmachine
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.5970149040222168
---
# vliegmachine
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### f117
#### f16
#### f18
 |
Denilson/gbert-base-germaner | [] | 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: mit
inference: False
---
# training logs
- https://wandb.ai/junyu/huggingface/runs/1jg2jlgt
# install
- https://github.com/JunnYu/FLASHQuad_pytorch
# usage
```python
import torch
from flash import FLASHForMaskedLM
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model = FLASHForMaskedLM.from_pretrained("junnyu/flash_small_wwm_cluecorpussmall")
model.eval()
text = "天气预报说今天的天[MASK]很好，那么我[MASK]一起去公园玩吧！"
inputs = tokenizer(text, return_tensors="pt", padding="max_length", max_length=512, return_token_type_ids=False) #这里必须是512，不然结果可能不对。
with torch.no_grad():
pt_outputs = model(**inputs).logits[0]
pt_outputs_sentence = "pytorch: "
for i, id in enumerate(tokenizer.encode(text)):
if id == tokenizer.mask_token_id:
val,idx = pt_outputs[i].softmax(-1).topk(k=5)
tokens = tokenizer.convert_ids_to_tokens(idx)
new_tokens = []
for v,t in zip(val.cpu(),tokens):
new_tokens.append(f"{t}+{round(v.item(),4)}")
pt_outputs_sentence += "[" + "||".join(new_tokens) + "]"
else:
pt_outputs_sentence += "".join(
tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True))
print(pt_outputs_sentence)
# pytorch: 天气预报说今天的天[气+0.994||天+0.0015||空+0.0014||晴+0.0005||阳+0.0003]很好，那么我[们+0.9563||就+0.0381||也+0.0032||俩+0.0004||来+0.0002]一起去公园玩吧！
``` |
Deniskin/essays_small_2000i | [] | 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
datasets:
- squad_es
- hackathon-pln-es/biomed_squad_es_v2
metrics:
- "f1"
---
# biomedtra-small for QA
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## Motivation
Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores the use of these new models to generate extractive Question Answering models for Biomedicine, and compares their effectiveness with general masked language models.
The models trained during the [Hackathon](https://somosnlp.org/hackathon) were:
[hackathon-pln-es/roberta-base-bne-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-bne-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es)
[hackathon-pln-es/biomedtra-small-es-squad2-es](https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es)
## Description
This model is a fine-tuned version of [mrm8488/biomedtra-small-es](https://huggingface.co/mrm8488/biomedtra-small-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset.
## Hyperparameters
The hyperparameters were chosen based on those used in [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2), an english-based model trained for similar purposes
```
--num_train_epochs 10 \
--learning_rate 1e-4 \
--max_seq_length 384 \
--doc_stride 128 \
```
## Performance
Evaluated on the [hackathon-pln-es/biomed_squad_es_v2](https://huggingface.co/datasets/hackathon-pln-es/biomed_squad_es_v2) dev set.
|Model |Base Model Domain|exact |f1 |HasAns_exact|HasAns_f1|NoAns_exact|NoAns_f1|
|--------------------------------------------------------------|-----------------|-------|-------|------------|---------|-----------|--------|
|hackathon-pln-es/roberta-base-bne-squad2-es |General |67.6341|75.6988|53.7367 |70.0526 |81.2174 |81.2174 |
|hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es|Biomedical |66.8426|75.2346|53.0249 |70.0031 |80.3478 |80.3478 |
|hackathon-pln-es/roberta-base-biomedical-es-squad2-es |Biomedical |67.6341|74.5612|47.6868 |61.7012 |87.1304 | 87.1304|
|hackathon-pln-es/biomedtra-small-es-squad2-es |Biomedical |34.4767|44.3294|45.3737 |65.307 |23.8261 |23.8261 |
## Team
Santiago Maximo: [smaximo](https://huggingface.co/smaximo) |
Deniskin/gpt3_medium | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | 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": 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
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi-colab
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-large-xls-r-300m-hindi-colab
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 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: 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: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
|
Denver/distilbert-base-uncased-finetuned-squad | [] | 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 | 2022-04-02T03:47:54Z | ---
language: es
datasets:
- squad_es
- hackathon-pln-es/biomed_squad_es_v2
metrics:
- "f1"
---
# roberta-base-biomedical-clinical-es for QA
This model was trained as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
## Motivation
Recent research has made available Spanish Language Models trained on Biomedical corpus. This project explores the use of these new models to generate extractive Question Answering models for Biomedicine, and compares their effectiveness with general masked language models.
The models trained during the [Hackathon](https://somosnlp.org/hackathon) were:
[hackathon-pln-es/roberta-base-bne-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-bne-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es)
[hackathon-pln-es/roberta-base-biomedical-es-squad2-es](https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es)
[hackathon-pln-es/biomedtra-small-es-squad2-es](https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es)
## Description
This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-biomedical-clinical-es](https://huggingface.co/PlanTL-GOB-ES/roberta-base-biomedical-clinical-es) on the [squad_es (v2)](https://huggingface.co/datasets/squad_es) training dataset.
## Hyperparameters
The hyperparameters were chosen based on those used in [PlanTL-GOB-ES/roberta-base-bne-sqac](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-sqac), a spanish-based QA model trained on a dataset with SQUAD v1 fromat.
```
--num_train_epochs 2
--learning_rate 3e-5
--weight_decay 0.01
--max_seq_length 386
--doc_stride 128
```
## Performance
Evaluated on the [hackathon-pln-es/biomed_squad_es_v2](https://huggingface.co/datasets/hackathon-pln-es/biomed_squad_es_v2) dev set.
|Model |Base Model Domain|exact |f1 |HasAns_exact|HasAns_f1|NoAns_exact|NoAns_f1|
|--------------------------------------------------------------|-----------------|-------|-------|------------|---------|-----------|--------|
|hackathon-pln-es/roberta-base-bne-squad2-es |General |67.6341|75.6988|53.7367 |70.0526 |81.2174 |81.2174 |
|hackathon-pln-es/roberta-base-biomedical-clinical-es-squad2-es|Biomedical |66.8426|75.2346|53.0249 |70.0031 |80.3478 |80.3478 |
|hackathon-pln-es/roberta-base-biomedical-es-squad2-es |Biomedical |67.6341|74.5612|47.6868 |61.7012 |87.1304 | 87.1304|
|hackathon-pln-es/biomedtra-small-es-squad2-es |Biomedical |34.4767|44.3294|45.3737 |65.307 |23.8261 |23.8261 |
## Team
Santiago Maximo: [smaximo](https://huggingface.co/smaximo) |
DeskDown/MarianMixFT_en-hi | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"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
}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-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. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6432
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7607 | 1.0 | 2334 | 3.6664 |
| 3.6323 | 2.0 | 4668 | 3.6461 |
| 3.6075 | 3.0 | 7002 | 3.6432 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
DeskDown/MarianMixFT_en-id | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"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
}
}
} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: paper_feedback_intent
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. -->
# paper_feedback_intent
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3621
- Accuracy: 0.9302
- Precision: 0.9307
- Recall: 0.9302
- F1: 0.9297
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.9174 | 1.0 | 11 | 0.7054 | 0.7907 | 0.7903 | 0.7907 | 0.7861 |
| 0.6917 | 2.0 | 22 | 0.4665 | 0.8140 | 0.8134 | 0.8140 | 0.8118 |
| 0.4276 | 3.0 | 33 | 0.3326 | 0.9070 | 0.9065 | 0.9070 | 0.9041 |
| 0.2656 | 4.0 | 44 | 0.3286 | 0.9070 | 0.9065 | 0.9070 | 0.9041 |
| 0.1611 | 5.0 | 55 | 0.3044 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.1025 | 6.0 | 66 | 0.3227 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0799 | 7.0 | 77 | 0.3216 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0761 | 8.0 | 88 | 0.3529 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0479 | 9.0 | 99 | 0.3605 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
| 0.0358 | 10.0 | 110 | 0.3621 | 0.9302 | 0.9307 | 0.9302 | 0.9297 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
DeskDown/MarianMixFT_en-ja | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"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
}
}
} | 9 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/780200431859269633/kXZwDd_Y_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Romantic Poetry Bot</div>
<div style="text-align: center; font-size: 14px;">@percybotshelley</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Romantic Poetry Bot.
| Data | Romantic Poetry Bot |
| --- | --- |
| Tweets downloaded | 3205 |
| Retweets | 0 |
| Short tweets | 20 |
| Tweets kept | 3185 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1bj4pakr/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @percybotshelley's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2yfs8v92) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2yfs8v92/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/percybotshelley')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Devid/DialoGPT-small-Miku | [
"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
}
}
} | 10 | null | ---
license: apache-2.0
tags:
- accelerator
metrics:
- accuracy
model-index:
- name: finetuned-vit-base-patch16-224-upside-down-detector
results: []
widget:
- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/original.jpg
example_title: original
- src: https://huggingface.co/jaygala24/finetuned-vit-base-patch16-224-upside-down-detector/resolve/main/upside_down.jpg
example_title: upside_down
---
# finetuned-vit-base-patch16-224-upside-down-detector
This model is a fine-tuned version of [vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom image orientation dataset adapted from the [beans](https://huggingface.co/datasets/beans) dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.8947
## Training and evaluation data
The custom dataset for image orientation adapted from [beans](https://huggingface.co/datasets/beans) dataset contains a total of 2,590 image samples with 1,295 original and 1,295 upside down. The model was fine-tuned on the train subset and evaluated on validation and test subsets. The dataset splits are listed below:
| Split | # examples |
|:----------:|:----------:|
| train | 2068 |
| validation | 133 |
| test | 128 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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: 32
- num_epochs: 5
### Training results
| Epoch | Accuracy |
|:----------:|:----------:|
| 0 | 0.8609 |
| 1 | 0.8835 |
| 2 | 0.8571 |
| 3 | 0.8941 |
| 4 | 0.8941 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.0+cu111
- Pytorch/XLA 1.9
- Datasets 2.0.0
- Tokenizers 0.12.0
|
DewiBrynJones/wav2vec2-large-xlsr-welsh | [
"cy",
"dataset:common_voice",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"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 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-general-v1-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v1)的蒸馏版本（仅4层 BERT），适用于**通用语义匹配**场景（此模型在 Chinese-STS 任务上效果较好，但在其它任务上效果并非最优，存在一定过拟合风险），比如文本特征抽取、文本向量聚类、文本语义搜索等业务场景。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 3% 左右（具体结果详见下文评估小节）。
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v1-distill')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```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 = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v1-distill')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v1-distill')
# 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
这里主要跟蒸馏前对应的 teacher 模型作了对比
*性能：*
| | Teacher | Student | Gap |
| ---------- | --------------------- | ------------------- | ----- |
| Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x |
| Cost | 23s | 12s | -47% |
| Latency | 37ms | 20ms | -46% |
| Throughput | 422 sentence/s | 788 sentence/s | 1.8x |
*精度:*
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** |
| -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- |
| **Teacher** | 84.54% | 82.17% | 23.80% | 65.94% | 45.52% | 11.52% | 48.51% | 51.71% |
| **Student** | 83.39% | 79.96% | 20.25% | 63.39% | 43.70% | 7.54% | 46.91% | 49.28% |
| **Gap** (abs.) | - | - | - | - | - | - | - | -2.43% |
*基于1万条数据测试，GPU设备是V100，batch_size=16，max_seq_len=256*
## Citing & Authors
E-mail: [email protected] |
Dhito/am | [] | 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
widget:
- text: 'define "toecoin": toecoin rose by 200% after Elon Musk mentioned it in his tweet'
datasets:
- 'marksverdhei/wordnet-definitions-en-2021'
---
# T5-define
(This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy)
This model is trained to generate word definitions based on the word and a context,
using a subset of wordnet for all words that have an example and definition.
The model uses task prompts on the format 'define "[word]": [example sentence]'
This model in particular is a one-shot learner for unseen words, as it has to infer the definition by only one example
How to run:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("marksverdhei/t5-base-define")
model = T5ForConditionalGeneration.from_pretrained("marksverdhei/t5-base-define")
prompt = "define \"noseplow\": The children hid as the noseplow drove across the street"
ids = tokenizer(prompt, return_tensors="pt").input_ids
generated_tokens = model.generate(ids)[0][1:-1]
print(tokenizer.decode(generated_tokens))
```
See the gist for the source code to used to train the model:
https://gist.github.com/marksverdhei/0a13f67e65460b71c05fcf558a6a91ae |
Dhruva/Interstellar | [] | 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 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-general-v2-distill
此模型是之前[开源通用语义匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2)的蒸馏版本（仅4层 BERT），适用于**通用语义匹配**场景，从效果来看该模型在各种任务上**泛化能力更好且编码速度更快**。
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 6% 左右（具体结果详见下文评估小节）。
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v2-distill')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```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 = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v2-distill')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v2-distill')
# 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
这里主要跟蒸馏前对应的 teacher 模型作了对比：
*性能：*
| | Teacher | Student | Gap |
| ---------- | --------------------- | ------------------- | ----- |
| Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x |
| Cost | 23s | 12s | -47% |
| Latency | 38ms | 20ms | -47% |
| Throughput | 418 sentence/s | 791 sentence/s | 1.9x |
*精度：*
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** |
| -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- |
| **Teacher** | 77.19% | 72.59% | 36.79% | 76.91% | 49.62% | 16.24% | 63.15% | 56.07% |
| **Student** | 76.49% | 73.33% | 26.46% | 64.26% | 46.02% | 11.83% | 52.45% | 50.12% |
| **Gap** (abs.) | - | - | - | - | - | - | - | -5.95% |
*基于1万条数据测试，GPU设备是V100，batch_size=16，max_seq_len=256*
## Citing & Authors
E-mail: [email protected] |
Dibyaranjan/nl_image_search | [] | 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 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- semantic-search
- chinese
---
# DMetaSoul/sbert-chinese-qmc-domain-v1
此模型是基于之前开源[问题匹配模型](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1)的蒸馏轻量化版本（仅含4层 BERT），适用于**开放领域的问题匹配**场景，比如：
- 洗澡用什么香皂好？vs. 洗澡用什么香皂好
- 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
- 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
离线训练好的大模型如果直接用于线上推理，对计算资源有苛刻的需求，而且难以满足业务环境对延迟、吞吐量等性能指标的要求，这里我们使用蒸馏手段来把大模型轻量化。从 12 层 BERT 蒸馏为 4 层后，模型参数量缩小到 44%，大概 latency 减半、throughput 翻倍、精度下降 4% 左右（具体结果详见下文评估小节）。
# Usage
## 1. Sentence-Transformers
通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
```
pip install -U sentence-transformers
```
然后使用下面的代码来载入该模型并进行文本表征向量的提取：
```python
from sentence_transformers import SentenceTransformer
sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-domain-v1')
embeddings = model.encode(sentences)
print(embeddings)
```
## 2. HuggingFace Transformers
如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
```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 = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
# 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
这里主要跟蒸馏前对应的 teacher 模型作了对比
*性能：*
| | Teacher | Student | Gap |
| ---------- | --------------------- | ------------------- | ----- |
| Model | BERT-12-layers (102M) | BERT-4-layers (45M) | 0.44x |
| Cost | 23s | 12s | -47% |
| Latency | 38ms | 20ms | -47% |
| Throughput | 421 sentence/s | 791 sentence/s | 1.9x |
*精度：*
| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | **Avg** |
| -------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | ------- |
| **Teacher** | 80.90% | 76.62% | 34.51% | 77.05% | 52.95% | 12.97% | 59.47% | 56.35% |
| **Student** | 79.89% | 76.34% | 27.59% | 69.26% | 49.40% | 9.06% | 53.52% | 52.15% |
| **Gap** (abs.) | - | - | - | - | - | - | - | -4.2% |
*基于1万条数据测试，GPU设备是V100，batch_size=16，max_seq_len=256*
## Citing & Authors
E-mail: [email protected] |
DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"dataset:wmt16",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | text2text-generation | {
"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
}
}
} | 1 | null | **Upside down detector**: Train a model to detect if images are upside down
* Trained on Google Street View.
* Synthetically turn some of images upside down. Create a training and test set.
* Build a neural network using TensorFlow.
* Train it to classify image orientation until a reasonable accuracy is reached.
* Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model's performance on these images in the future.
*The code is taken from: [RotNet](https://github.com/d4nst/RotNet), with minor changes.* |
DiegoBalam12/institute_classification | [] | 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
tags:
- generated_from_trainer
model-index:
- name: wav2vec_asr_swbd_10_epochs
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. -->
# wav2vec_asr_swbd_10_epochs
This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 0.9627
## 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: 8
- 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_steps: 1000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 1.0682 | 0.22 | 5000 | 0.7383 | 0.4431 |
| 0.9143 | 0.44 | 10000 | 0.7182 | 0.4058 |
| 0.8905 | 0.66 | 15000 | 0.6291 | 0.3987 |
| 0.8354 | 0.87 | 20000 | 0.5976 | 0.3954 |
| 0.7749 | 1.09 | 25000 | 0.5773 | 0.3901 |
| 0.7336 | 1.31 | 30000 | 0.5812 | 0.3871 |
| 0.7314 | 1.53 | 35000 | 0.5802 | 0.3895 |
| 0.0 | 1.75 | 40000 | nan | 0.9627 |
| 0.0 | 1.97 | 45000 | nan | 0.9627 |
| 0.0 | 2.19 | 50000 | nan | 0.9627 |
| 0.0 | 2.4 | 55000 | nan | 0.9627 |
| 0.0 | 2.62 | 60000 | nan | 0.9627 |
| 0.0 | 2.84 | 65000 | nan | 0.9627 |
| 0.0 | 3.06 | 70000 | nan | 0.9627 |
| 0.0 | 3.28 | 75000 | nan | 0.9627 |
| 0.0 | 3.5 | 80000 | nan | 0.9627 |
| 0.0 | 3.72 | 85000 | nan | 0.9627 |
| 0.0 | 3.93 | 90000 | nan | 0.9627 |
| 0.0 | 4.15 | 95000 | nan | 0.9627 |
| 0.0 | 4.37 | 100000 | nan | 0.9627 |
| 0.0 | 4.59 | 105000 | nan | 0.9627 |
| 0.0 | 4.81 | 110000 | nan | 0.9627 |
| 0.0 | 5.03 | 115000 | nan | 0.9627 |
| 0.0 | 5.25 | 120000 | nan | 0.9627 |
| 0.0 | 5.46 | 125000 | nan | 0.9627 |
| 0.0 | 5.68 | 130000 | nan | 0.9627 |
| 0.0 | 5.9 | 135000 | nan | 0.9627 |
| 0.0 | 6.12 | 140000 | nan | 0.9627 |
| 0.0 | 6.34 | 145000 | nan | 0.9627 |
| 0.0 | 6.56 | 150000 | nan | 0.9627 |
| 0.0 | 6.78 | 155000 | nan | 0.9627 |
| 0.0 | 7.0 | 160000 | nan | 0.9627 |
| 0.0 | 7.21 | 165000 | nan | 0.9627 |
| 0.0 | 7.43 | 170000 | nan | 0.9627 |
| 0.0 | 7.65 | 175000 | nan | 0.9627 |
| 0.0 | 7.87 | 180000 | nan | 0.9627 |
| 0.0 | 8.09 | 185000 | nan | 0.9627 |
| 0.0 | 8.31 | 190000 | nan | 0.9627 |
| 0.0 | 8.53 | 195000 | nan | 0.9627 |
| 0.0 | 8.74 | 200000 | nan | 0.9627 |
| 0.0 | 8.96 | 205000 | nan | 0.9627 |
| 0.0 | 9.18 | 210000 | nan | 0.9627 |
| 0.0 | 9.4 | 215000 | nan | 0.9627 |
| 0.0 | 9.62 | 220000 | nan | 0.9627 |
| 0.0 | 9.84 | 225000 | nan | 0.9627 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
Digakive/Hsgshs | [] | 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: mit
tags:
- text-classification
- PyTorch
- Transformers
---
# fakeBert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on a [news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset) from Kaggle.
## Model description
Fine-tuning Bert for text classification.
## Training and evaluation data
Training & Validation: [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
Testing: [Fake News Detection Challenge KDD 2020](https://www.kaggle.com/competitions/fakenewskdd2020/overview)
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-5
- train_batch_size: 16
- eval_batch_size: 16
- optimizer: AdamW
|
DimaOrekhov/transformer-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"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_keras_callback
model-index:
- name: JustAdvanceTechonology/medical_notes_mulitilingual
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# JustAdvanceTechonology/medical_notes_mulitilingual
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.7536
- Validation Loss: 6.1397
- Epoch: 7
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 1209, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 11.2097 | 6.1454 | 0 |
| 8.7069 | 6.1880 | 1 |
| 8.7350 | 6.1834 | 2 |
| 8.7021 | 6.1364 | 3 |
| 8.7385 | 6.2117 | 4 |
| 8.7318 | 6.2004 | 5 |
| 8.7487 | 6.1531 | 6 |
| 8.7536 | 6.1397 | 7 |
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.5.0
- Datasets 2.0.0
- Tokenizers 0.10.1
|
Dizoid/Lll | [] | 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
---
## Dataset
[NEWS2018 DATASET_04, Task ID: M-EnHi](http://workshop.colips.org/news2018/dataset.html)
## Notebooks
- `xmltodict.ipynb` contains the code to convert the `xml` files to `json` for training
- `training_script.ipynb` contains the code for training and inference. It is a modified version of https://github.com/AI4Bharat/IndianNLP-Transliteration/blob/master/NoteBooks/Xlit_TrainingSetup_condensed.ipynb
## Predictions
`pred_test.json` contains top-10 predictions on the validation set of the dataset
## Evaluation Scores on validation set
TOP 10 SCORES FOR 1000 SAMPLES
|Metrics | Score |
|-----------|-----------|
|ACC | 0.703000|
|Mean F-score| 0.949289|
|MRR | 0.486549|
|MAP_ref | 0.381000|
TOP 5 SCORES FOR 1000 SAMPLES:
|Metrics | Score |
|-----------|-----------|
|ACC |0.621000|
|Mean F-score |0.937985|
|MRR |0.475033|
|MAP_ref |0.381000|
TOP 3 SCORES FOR 1000 SAMPLES:
|Metrics | Score |
|-----------|-----------|
|ACC |0.560000|
|Mean F-score |0.927025|
|MRR |0.461333|
|MAP_ref |0.381000|
TOP 2 SCORES FOR 1000 SAMPLES:
|Metrics | Score |
|-----------|-----------|
|ACC | 0.502000|
|Mean F-score | 0.913697|
|MRR | 0.442000|
|MAP_ref | 0.381000|
TOP 1 SCORES FOR 1000 SAMPLES:
|Metrics | Score |
|-----------|-----------|
|ACC | 0.382000|
|Mean F-score | 0.881272|
|MRR | 0.382000|
|MAP_ref | 0.380500| |
Dkwkk/W | [] | 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
thumbnail: http://www.huggingtweets.com/sanjabh/1648901691950/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1484080880222351360/FtDB2j4B_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lucid Dreams</div>
<div style="text-align: center; font-size: 14px;">@sanjabh</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Lucid Dreams.
| Data | Lucid Dreams |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 373 |
| Short tweets | 137 |
| Tweets kept | 2740 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s7tzf32/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sanjabh's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1cl1cjnx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1cl1cjnx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/sanjabh')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Waynehillsdev/waynehills_sentimental_kor | [
"pytorch",
"electra",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"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
}
}
} | 33 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.5925
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 5.9198 |
| No log | 2.0 | 4 | 5.7019 |
| No log | 3.0 | 6 | 5.5925 |
### Framework versions
- Transformers 4.11.0
- Pytorch 1.10.2+cpu
- Datasets 2.0.0
- Tokenizers 0.10.3
|
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-25 | [
"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
}
}
} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2208
- Accuracy: 0.924
- F1: 0.9240
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8538 | 1.0 | 250 | 0.3317 | 0.904 | 0.8999 |
| 0.2599 | 2.0 | 500 | 0.2208 | 0.924 | 0.9240 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6
|
DoyyingFace/bert-asian-hate-tweets-asonam-clean | [
"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 | ---
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: 0.5598704865754364
---
<!-- 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: 0.8697
- Matthews Correlation: 0.5599
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5223 | 1.0 | 535 | 0.5444 | 0.4309 |
| 0.3457 | 2.0 | 1070 | 0.5213 | 0.5021 |
| 0.2351 | 3.0 | 1605 | 0.6793 | 0.5234 |
| 0.1693 | 4.0 | 2140 | 0.7587 | 0.5527 |
| 0.1301 | 5.0 | 2675 | 0.8697 | 0.5599 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 26,792 | 2022-04-02T21:07:10Z | distilbert-base-uncased trained for 250K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 341 | 2022-04-02T21:12:40Z | distilbert-base-uncased trained for 500K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 2,973 | 2022-04-02T21:15:23Z | distilbert-base-uncased trained for 750K steps with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 42,640 | null | distilbert-base-uncased trained for 680K steps (lowest loss on dev dataset) with batch size 64 on C4, MSMARCO, Wikipedia, S2ORC, News
|
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 11,644 | 2022-04-02T21:45:00Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8480392156862745
- name: F1
type: f1
value: 0.89419795221843
---
<!-- 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-mrpc
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: 0.4044
- Accuracy: 0.8480
- F1: 0.8942
## 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 230 | 0.3830 | 0.8162 | 0.8673 |
| No log | 2.0 | 460 | 0.3957 | 0.8456 | 0.8952 |
| 0.4307 | 3.0 | 690 | 0.4044 | 0.8480 | 0.8942 |
| 0.4307 | 4.0 | 920 | 0.5649 | 0.8407 | 0.8915 |
| 0.1739 | 5.0 | 1150 | 0.5983 | 0.8480 | 0.8956 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 8,621,271 | 2022-04-02T22:08:33Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: distilbert-base-uncased-finetuned-stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.8636303639161342
---
<!-- 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-stsb
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: 0.5644
- Pearson: 0.8666
- Spearmanr: 0.8636
## 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 | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| No log | 1.0 | 360 | 0.6366 | 0.8537 | 0.8516 |
| 1.0464 | 2.0 | 720 | 0.6171 | 0.8632 | 0.8626 |
| 0.4002 | 3.0 | 1080 | 0.6082 | 0.8663 | 0.8643 |
| 0.4002 | 4.0 | 1440 | 0.5644 | 0.8666 | 0.8636 |
| 0.2479 | 5.0 | 1800 | 0.5780 | 0.8654 | 0.8624 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 175,983 | 2022-04-02T22:29:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: canine-s-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: 0.059386434587477076
---
<!-- 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. -->
# canine-s-finetuned-cola
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6653
- Matthews Correlation: 0.0594
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6132 | 1.0 | 535 | 0.6289 | 0.0 |
| 0.6062 | 2.0 | 1070 | 0.6179 | 0.0 |
| 0.6122 | 3.0 | 1605 | 0.6160 | 0.0 |
| 0.5939 | 4.0 | 2140 | 0.6159 | 0.0 |
| 0.5721 | 5.0 | 2675 | 0.6653 | 0.0594 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 1,814 | 2022-04-02T23:02:39Z | ---
language: en
thumbnail: http://www.huggingtweets.com/clortown-elonmusk-stephencurry30/1648940589601/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488574779351187458/RlIQNUFG_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1484233608793518081/tOID8aXq_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & yeosang elf agenda & Stephen Curry</div>
<div style="text-align: center; font-size: 14px;">@clortown-elonmusk-stephencurry30</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon Musk & yeosang elf agenda & Stephen Curry.
| Data | Elon Musk | yeosang elf agenda | Stephen Curry |
| --- | --- | --- | --- |
| Tweets downloaded | 221 | 3143 | 3190 |
| Retweets | 7 | 541 | 384 |
| Short tweets | 62 | 463 | 698 |
| Tweets kept | 152 | 2139 | 2108 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2sqcbnn5/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @clortown-elonmusk-stephencurry30's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1mq1ftjh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1mq1ftjh/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/clortown-elonmusk-stephencurry30')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fi",
"fr",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"he",
"hi",
"hu",
"is",
"io",
"id",
"ga",
"it",
"ja",
"jv",
"kn",
"kk",
"ky",
"ko",
"la",
"lv",
"lt",
"roa",
"nds",
"lm",
"mk",
"mg",
"ms",
"ml",
"mr",
"min",
"ne",
"new",
"nb",
"nn",
"oc",
"fa",
"pms",
"pl",
"pt",
"pa",
"ro",
"ru",
"sco",
"sr",
"scn",
"sk",
"sl",
"aze",
"es",
"su",
"sw",
"sv",
"tl",
"tg",
"ta",
"tt",
"te",
"tr",
"uk",
"ud",
"uz",
"vi",
"vo",
"war",
"cy",
"fry",
"pnb",
"yo",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 328,585 | null | ---
language:
- es
- qu
tags:
- quechua
- translation
- spanish
license: apache-2.0
metrics:
- bleu
- sacrebleu
widget:
- text: "Dios ama a los hombres"
- text: "A pesar de todo, soy feliz"
- text: "¿Qué harán allí?"
- text: "Debes aprender a respetar"
---
# Spanish to Quechua translator
This model is a finetuned version of the [t5-small](https://huggingface.co/t5-small).
## Model description
t5-small-finetuned-spanish-to-quechua has trained for 46 epochs with 102 747 sentences, the validation was performed with 12 844 sentences and 12 843 sentences were used for the test.
## Intended uses & limitations
A large part of the dataset has been extracted from biblical texts, which makes the model perform better with certain types of sentences.
### How to use
You can import this model as follows:
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> model_name = 'hackathon-pln-es/t5-small-finetuned-spanish-to-quechua'
>>> model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
```
To translate you can do:
```python
>>> sentence = "Entonces dijo"
>>> input = tokenizer(sentence, return_tensors="pt")
>>> output = model.generate(input["input_ids"], max_length=40, num_beams=4, early_stopping=True)
>>> print('Original Sentence: {} \nTranslated sentence: {}'.format(sentence, tokenizer.decode(output[0])))
```
### Limitations and bias
Actually this model only can translate to Quechua of Ayacucho.
## Training data
For train this model we use [Spanish to Quechua dataset](https://huggingface.co/datasets/hackathon-pln-es/spanish-to-quechua)
## Evaluation results
We obtained the following metrics during the training process:
- eval_bleu = 2.9691
- eval_loss = 1.2064628601074219
## Team members
- [Sara Benel](https://huggingface.co/sbenel)
- [Jose Vílchez](https://huggingface.co/JCarlos)
|
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 2,316 | 2022-04-03T00:55:24Z | ---
license: apache-2.0
---
# -*- coding: utf-8 -*-
'''
Original file is located at
https://colab.research.google.com/drive/1HrNm5UMZr2Zjmze_HKW799p6LAHM8BTa
'''
from google.colab import files
files.upload()
!pip install kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download 'shaunthesheep/microsoft-catsvsdogs-dataset'
!unzip microsoft-catsvsdogs-dataset
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
image_dir='/content/PetImages/Cat'
!mkdir train_folder
!mkdir test_folder
import os
path='/content/train_folder/'
dir='upside_down'
dir2='normal'
training_normal= os.path.join(path, dir2)
training_upside= os.path.join(path, dir)
os.mkdir(training_normal)
os.mkdir(training_upside)
#creating classes directories
path='/content/test_folder/'
dir='upside_down'
dir2='normal'
training_normal= os.path.join(path, dir2)
training_upside= os.path.join(path, dir)
os.mkdir(training_normal)
os.mkdir(training_upside)
#copying only the cat images to my train folder
fnames = ['{}.jpg'.format(i) for i in range(2000)]
for fname in fnames:
src = os.path.join('/content/PetImages/Cat', fname)
dst = os.path.join('/content/train_folder/normal', fname)
shutil.copyfile(src, dst)
import os
import shutil
fnames = ['{}.jpg'.format(i) for i in range(2000, 4000)]
for fname in fnames:
src = os.path.join('/content/PetImages/Cat', fname)
dst = os.path.join('/content/test_folder/normal', fname)
shutil.copyfile(src, dst)
from scipy import ndimage, misc
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import imageio
import os
import cv2
#inverting Training Images
outPath = '/content/train_folder/upside_down'
path ='/content/train_folder/normal'
# iterate through the names of contents of the folder
for image_path in os.listdir(path):
# create the full input path and read the file
input_path = os.path.join(path, image_path)
image_to_rotate =plt.imread(input_path)
# rotate the image
rotated = np.flipud(image_to_rotate)
# create full output path, 'example.jpg'
# becomes 'rotate_example.jpg', save the file to disk
fullpath = os.path.join(outPath, 'rotated_'+image_path)
imageio.imwrite(fullpath, rotated)
#nverting images for Validation
outPath = '/content/test_folder/upside_down'
path ='/content/test_folder/normal'
# iterate through the names of contents of the folder
for image_path in os.listdir(path):
# create the full input path and read the file
input_path = os.path.join(path, image_path)
image_to_rotate =plt.imread(input_path)
# rotate the image
rotated = np.flipud(image_to_rotate)
# create full output path, 'example.jpg'
# becomes 'rotate_example.jpg', save the file to disk
fullpath = os.path.join(outPath, 'rotated_'+image_path)
imageio.imwrite(fullpath, rotated)
ima='/content/train_folder/inverted/rotated_1001.jpg'
image=plt.imread(ima)
plt.imshow(image)
# visualize the the figure
plt.show()
train_dir='/content/train_folder'
train_gen=ImageDataGenerator(rescale=1./255)
train_images= train_gen.flow_from_directory(
train_dir,
target_size=(250,250),
batch_size=50,
class_mode='binary'
)
validation_dir='/content/test_folder'
test_gen=ImageDataGenerator(rescale=1./255)
test_images= test_gen.flow_from_directory(
validation_dir,
target_size=(250,250),
batch_size=50,
class_mode='binary'
)
model=tf.keras.Sequential([
tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(250,250,3)),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
from tensorflow.keras.optimizers import RMSprop
model.compile(optimizer=RMSprop(learning_rate=0.001), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['acc'])
history=model.fit(train_images, validation_data=test_images, epochs=5, steps_per_epoch=40 )
|
xlm-roberta-large-finetuned-conll03-german | [
"pytorch",
"rust",
"xlm-roberta",
"token-classification",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:1911.02116",
"arxiv:1910.09700",
"transformers",
"autotrain_compatible",
"has_space"
] | 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
}
}
} | 3,929 | 2022-04-03T14:54:33Z | A version of https://huggingface.co/johnowhitaker/orbgan_e1 trained on only dark images |
ARATHI/electra-small-discriminator-fintuned-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 | ---
license: mit
---
### Dataset used
[Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
### Labels
Fake news: 1 </br>
Real news: 0
### Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import torch
config = AutoConfig.from_pretrained("bhavitvyamalik/fake-news_xtremedistil-l6-h256-uncased")
model = AutoModelForSequenceClassification.from_pretrained("bhavitvyamalik/fake-news_xtremedistil-l6-h256-uncased", config=config)
tokenizer = AutoTokenizer.from_pretrained("microsoft/xtremedistil-l6-h256-uncased", usefast=True)
text = "According to reports by Fox News, Biden is the President of the USA"
encode = tokenizer(text, max_length=512, truncation=True, padding="max_length", return_tensors="pt")
output = model(**encode)
print(torch.argmax(output["logits"]))
```
### Performance on test data
```json
'test/accuracy': 0.9977836608886719,
'test/aucroc': 0.9999998807907104,
'test/f1': 0.9976308941841125,
'test/loss': 0.00828308891505003
```
### Run can be tracked here
[Wandb project for Fake news classifier](https://wandb.ai/bhavitvya/Fake%20news%20classifier?workspace=user-bhavitvya) |
Ab0/autoencoder-keras-mnist-demo | [
"keras"
] | 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
}
}
} | 2 | 2022-04-04T18:59:04Z | ---
license: apache-2.0
datasets:
- eurosat
widget:
- src: forest.png
example_title: Forest
---
# ConvNext fine-tuned on Eurosat
This model is a `facebook/convnext-tiny-224` model fine-tuned on the [Eurosat dataset](https://github.com/phelber/EuroSAT). |
AdapterHub/bert-base-uncased-pf-hotpotqa | [
"bert",
"en",
"dataset:hotpot_qa",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering"
] | question-answering | {
"architectures": null,
"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
}
}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: opus-mt-ar-en-finetunedTanzil-v7-ar-to-en
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# opus-mt-ar-en-finetunedTanzil-v7-ar-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1919
- Validation Loss: 0.5047
- Train Rouge1: 49.6877
- Train Rouge2: 25.9574
- Train Rougel: 45.2590
- Train Rougelsum: 45.7464
- Train Gen Len: 85.57
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 0.1959 | 0.5105 | 48.2182 | 23.4978 | 44.1127 | 44.6422 | 87.45 | 0 |
| 0.1950 | 0.5114 | 49.5777 | 25.1663 | 45.7183 | 46.0930 | 86.72 | 1 |
| 0.1937 | 0.5074 | 49.1793 | 24.1899 | 45.3374 | 45.5902 | 84.805 | 2 |
| 0.1929 | 0.5075 | 49.1553 | 24.8199 | 44.7342 | 45.1392 | 87.495 | 3 |
| 0.1919 | 0.5047 | 49.6877 | 25.9574 | 45.2590 | 45.7464 | 85.57 | 4 |
### Framework versions
- Transformers 4.17.0.dev0
- TensorFlow 2.7.0
- Datasets 1.18.4.dev0
- Tokenizers 0.10.3
|
AkshatSurolia/ConvNeXt-FaceMask-Finetuned | [
"pytorch",
"safetensors",
"convnext",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | image-classification | {
"architectures": [
"ConvNextForImageClassification"
],
"model_type": "convnext",
"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
}
}
} | 56 | null | ---
language: multilingual
tags:
- emotion
- emotion-analysis
- multilingual
widget:
- text: "Guarda! ci sono dei bellissimi capibara!"
example_title: "Emotion Classification 1"
- text: "Sei una testa di cazzo!!"
example_title: "Emotion Classification 2"
- text: "Quelle bonne nouvelle!"
example_title: "Emotion Classification 3"
arxiv: ""
---
#
[Federico Bianchi](https://federicobianchi.io/) •
[Debora Nozza](http://dnozza.github.io/) •
[Dirk Hovy](http://www.dirkhovy.com/)
## Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.
## Model
This model is the fine-tuned version of the [XLM-T](https://aclanthology.org/2022.lrec-1.27/) model.
### Intended Use
The model is intended as a research output for research communities.
#### Primary intended uses
The primary intended users of these models are AI researchers.
## Results
This model had an F1 of 0.85 on the test set.
## License
For models, restrictions may apply to the data (which are derived from existing datasets) or Twitter (main data source).
We refer users to the original licenses accompanying each dataset and Twitter regulations.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
## Citation
Please use the following BibTeX entry if you use this model in your project:
```
@inproceedings{bianchi2021feel,
title = "{XLM-EMO: Multilingual Emotion Prediction in Social Media Text}",
author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
year = "2022",
publisher = "Association for Computational Linguistics",
}
``` |
AkshaySg/langid | [
"multilingual",
"dataset:VoxLingua107",
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] | audio-classification | {
"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
}
}
} | 2 | null | ---
language: en
license: apache-2.0
tags:
- text-classfication
- int8
- Intel® Neural Compressor
- QuantizationAwareTraining
datasets:
- mrpc
metrics:
- f1
---
# INT8 BERT base uncased finetuned MRPC
### QuantizationAwareTraining
This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc).
### Test result
| |INT8|FP32|
|---|:---:|:---:|
| **Accuracy (eval-f1)** |0.9142|0.9042|
| **Model size (MB)** |107|418|
### Load with optimum:
```python
from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification
int8_model = IncQuantizedModelForSequenceClassification(
'Intel/bert-base-uncased-mrpc-int8-qat',
)
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- train_batch_size: 8
- eval_batch_size: 8
- eval_steps: 100
- load_best_model_at_end: True
- metric_for_best_model: f1
- early_stopping_patience = 6
- early_stopping_threshold = 0.001
|
Al/mymodel | [] | 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
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bert-base-chinese-complaint-128
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- 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-chinese-complaint-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3004
## 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: 5e-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: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.3735 | 1.0 | 1250 | 2.4628 |
| 2.2412 | 2.0 | 2500 | 2.0378 |
| 1.9251 | 3.0 | 3750 | 1.8368 |
| 1.7407 | 4.0 | 5000 | 1.6972 |
| 1.6137 | 5.0 | 6250 | 1.5937 |
| 1.5365 | 6.0 | 7500 | 1.5315 |
| 1.4662 | 7.0 | 8750 | 1.4921 |
| 1.3985 | 8.0 | 10000 | 1.4517 |
| 1.3509 | 9.0 | 11250 | 1.4308 |
| 1.3047 | 10.0 | 12500 | 1.3906 |
| 1.2745 | 11.0 | 13750 | 1.3467 |
| 1.2377 | 12.0 | 15000 | 1.3306 |
| 1.2139 | 13.0 | 16250 | 1.3205 |
| 1.2027 | 14.0 | 17500 | 1.3098 |
| 1.1722 | 15.0 | 18750 | 1.2845 |
| 1.1697 | 16.0 | 20000 | 1.3004 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.7.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Alaeddin/convbert-base-turkish-ner-cased | [
"pytorch",
"convbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"ConvBertForTokenClassification"
],
"model_type": "convbert",
"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
}
}
} | 9 | null | ---
tags:
- espnet
- audio
- text-to-speech
language: gos
---
# Tacotron2 Gronings
|
AlanDev/test | [] | 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
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: 0.925
- name: F1
type: f1
value: 0.9250750482655898
---
<!-- 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: 0.2236
- Accuracy: 0.925
- F1: 0.9251
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8341 | 1.0 | 250 | 0.3329 | 0.8985 | 0.8950 |
| 0.2562 | 2.0 | 500 | 0.2236 | 0.925 | 0.9251 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Aleksandar/bert-srb-base-cased-oscar | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"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
}
}
} | 7 | null | ---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: hubert-base-common-language
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. -->
# hubert-base-common-language
This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3477
- Accuracy: 0.7317
## 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: 1
- eval_batch_size: 4
- seed: 0
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10.0
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Aleksandar/bert-srb-ner-setimes-lr | [] | 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
datasets:
- msp-podcast
inference: true
tags:
- speech
- audio
- wav2vec2
- audio-classification
- emotion-recognition
license: cc-by-nc-sa-4.0
---
# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning [
Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust) on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An [ONNX](https://onnx.ai/") export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127). Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
# Usage
```python
import numpy as np
import torch
import torch.nn as nn
from transformers import Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Model,
Wav2Vec2PreTrainedModel,
)
class RegressionHead(nn.Module):
r"""Classification head."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.final_dropout)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, features, **kwargs):
x = features
x = self.dropout(x)
x = self.dense(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class EmotionModel(Wav2Vec2PreTrainedModel):
r"""Speech emotion classifier."""
def __init__(self, config):
super().__init__(config)
self.config = config
self.wav2vec2 = Wav2Vec2Model(config)
self.classifier = RegressionHead(config)
self.init_weights()
def forward(
self,
input_values,
):
outputs = self.wav2vec2(input_values)
hidden_states = outputs[0]
hidden_states = torch.mean(hidden_states, dim=1)
logits = self.classifier(hidden_states)
return hidden_states, logits
# load model from hub
device = 'cpu'
model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = EmotionModel.from_pretrained(model_name)
# dummy signal
sampling_rate = 16000
signal = np.zeros((1, sampling_rate), dtype=np.float32)
def process_func(
x: np.ndarray,
sampling_rate: int,
embeddings: bool = False,
) -> np.ndarray:
r"""Predict emotions or extract embeddings from raw audio signal."""
# run through processor to normalize signal
# always returns a batch, so we just get the first entry
# then we put it on the device
y = processor(x, sampling_rate=sampling_rate)
y = y['input_values'][0]
y = torch.from_numpy(y).to(device)
# run through model
with torch.no_grad():
y = model(y)[0 if embeddings else 1]
# convert to numpy
y = y.detach().cpu().numpy()
return y
process_func(signal, sampling_rate)
# Arousal dominance valence
# [[0.5460759 0.6062269 0.4043165]]
process_func(signal, sampling_rate, embeddings=True)
# Pooled hidden states of last transformer layer
# [[-0.00752167 0.0065819 -0.00746339 ... 0.00663631 0.00848747
# 0.00599209]]
```
|
Aleksandar/electra-srb-ner | [
"pytorch",
"safetensors",
"electra",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"ElectraForTokenClassification"
],
"model_type": "electra",
"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 | ---
language:
- es
tags:
- biomedical
- clinical
- eHR
- spanish
license: apache-2.0
datasets:
- "PlanTL-GOB-ES/pharmaconer"
metrics:
- f1
model-index:
- name: PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer
results:
- task:
type: token-classification
dataset:
name: pharmaconer
type: PlanTL-GOB-ES/pharmaconer
metrics:
- name: f1
type: f1
value: 0.8913
widget:
- text: "Se realizó estudio analítico destacando incremento de niveles de PTH y vitamina D (103,7 pg/ml y 272 ng/ml, respectivamente), atribuidos al exceso de suplementación de vitamina D."
- text: " Por el hallazgo de múltiples fracturas por estrés, se procedió a estudio en nuestras consultas, realizándose análisis con función renal, calcio sérico y urinario, calcio iónico, magnesio y PTH, que fueron normales."
- text: "Se solicitó una analítica que incluía hemograma, bioquímica, anticuerpos antinucleares (ANA) y serologías, examen de orina, así como biopsia de la lesión. Los resultados fueron normales, con ANA, anti-Sm, anti-RNP, anti-SSA, anti-SSB, anti-Jo1 y anti-Scl70 negativos."
---
# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the PharmaCoNER dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Evaluation](#evaluation)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Citing information](#citing-information)
- [Disclaimer](#disclaimer)
</details>
## Model description
A fine-tuned version of the [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish biomedical corpus known to date, composed of biomedical documents, clinical cases and EHR documents for a total of 1.1B tokens of clean and deduplicated text processed.
For more details about the corpora and training, check the _bsc-bio-ehr-es_ model card.
## Intended uses and limitations
## How to use
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
The dataset used is [PharmaCoNER](https://huggingface.co/datasets/PlanTL-GOB-ES/pharmaconer), a NER dataset annotated with substances, compounds and proteins entities. For further information, check the [official website](https://temu.bsc.es/pharmaconer/).
## Evaluation
F1 Score: 0.8913
For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
### Contact information
For further information, send an email to <[email protected]>
### Copyright
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
## Citing information
If you use these models, please cite our work:
```bibtext
@inproceedings{carrino-etal-2022-pretrained,
title = "Pretrained Biomedical Language Models for Clinical {NLP} in {S}panish",
author = "Carrino, Casimiro Pio and
Llop, Joan and
P{\`a}mies, Marc and
Guti{\'e}rrez-Fandi{\~n}o, Asier and
Armengol-Estap{\'e}, Jordi and
Silveira-Ocampo, Joaqu{\'\i}n and
Valencia, Alfonso and
Gonzalez-Agirre, Aitor and
Villegas, Marta",
booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.bionlp-1.19",
doi = "10.18653/v1/2022.bionlp-1.19",
pages = "193--199",
abstract = "This work presents the first large-scale biomedical Spanish language models trained from scratch, using large biomedical corpora consisting of a total of 1.1B tokens and an EHR corpus of 95M tokens. We compared them against general-domain and other domain-specific models for Spanish on three clinical NER tasks. As main results, our models are superior across the NER tasks, rendering them more convenient for clinical NLP applications. Furthermore, our findings indicate that when enough data is available, pre-training from scratch is better than continual pre-training when tested on clinical tasks, raising an exciting research question about which approach is optimal. Our models and fine-tuning scripts are publicly available at HuggingFace and GitHub.",
}
```
### Disclaimer
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence.
In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos. |
Aleksandar/electra-srb-oscar | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"ElectraForMaskedLM"
],
"model_type": "electra",
"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 | ---
language: en
thumbnail: http://www.huggingtweets.com/chrismedlandf1-elonmusk-scarbstech/1649253035547/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/456005573/scarbs_400x400.JPG')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1252178304192389120/bXT3lbuR_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Craig Scarborough & Chris Medland</div>
<div style="text-align: center; font-size: 14px;">@chrismedlandf1-elonmusk-scarbstech</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon Musk & Craig Scarborough & Chris Medland.
| Data | Elon Musk | Craig Scarborough | Chris Medland |
| --- | --- | --- | --- |
| Tweets downloaded | 2621 | 3249 | 3250 |
| Retweets | 116 | 387 | 196 |
| Short tweets | 795 | 646 | 102 |
| Tweets kept | 1710 | 2216 | 2952 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3m6vm0tf/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @chrismedlandf1-elonmusk-scarbstech's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/mnfs00gg) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/mnfs00gg/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/chrismedlandf1-elonmusk-scarbstech')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Aleksandar1932/gpt2-pop | [
"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 | null | ---
license: mit
---
Base model: [roberta-large](https://huggingface.co/roberta-large)
Fine tuned for persuadee donation detection on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019):
Given a complete dialogue from Persuasion For Good, the task is to predict the binary label:
- 0: the persuadee does not intend to donate
- 1: the persuadee intends to donate
Only persuadee utterances are input to the model for this task - persuader utterances are discarded. Each training example is the concatenation of all persuadee utterances in a single dialogue, each separated by the `</s>` token.
For example:
**Input**: `<s>How are you?</s>Can you tell me more about the charity?</s>...</s>Sure, I'll donate a dollar.</s>...</s>`
**Label**: 1
**Input**: `<s>How are you?</s>Can you tell me more about the charity?</s>...</s>I am not interested.</s>...</s>`
**Label**: 0
The following Dialogues were excluded:
- 146 dialogues where a donation of 0 was made at the end of the task but a non-zero amount was pledged by the persuadee in the dialogue, per the following regular expression: `(?:\$(?:0\.)?[1-9]|[1-9][.0-9]*?(?: ?\$| dollars?| cents?))`
Data Info:
- **Training set**: 587 dialogues, using actual end-task donations as labels
- **Validation set**: 141 dialogues, using manual donation intention labels from Persuasion For Good 'AnnSet'
- **Test set**: 143 dialogues, using manual donation intention labels from Persuasion For Good 'AnnSet'
Training Info:
- **Loss**: CrossEntropy with class weights: 1.5447 (class 0) and 0.7393 (class 1). These weights were derived from the training split.
- **Early Stopping**: The checkpoint with the highest validation macro f1 was selected. This occurred at step 35 (see training metrics for more detail).
Testing Info:
- **Test Macro F1**: 0.893
- **Test Accuracy**: 0.902 |
AlekseyKorshuk/bert | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | 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 |
---
tags:
- SpaceInvadersNoFrameskip-v4
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=1181.00 +/- 93.8296328459192
## Usage (with Stable-baselines3)
TODO: Add your code
---
tags:
- SpaceInvadersNoFrameskip-v4
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=1190.50 +/- 114.1807777167418
## Usage (with Stable-baselines3)
TODO: Add your code
---
tags:
- SpaceInvadersNoFrameskip-v4
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=1147.50 +/- 39.82775414205526
## Usage (with Stable-baselines3)
TODO: Add your code
---
tags:
- SpaceInvadersNoFrameskip-v4
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=1197.00 +/- 125.76167937809991
## Usage (with Stable-baselines3)
TODO: Add your code
---
tags:
- SpaceInvadersNoFrameskip-v4
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=1261.00 +/- 149.81321704042003
## Usage (with Stable-baselines3)
TODO: Add your code
---
tags:
- SpaceInvadersNoFrameskip-v4
---
# **PPO** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **PPO** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=1246.00 +/- 128.81770064707723
## Usage (with Stable-baselines3)
TODO: Add your code
|
AlekseyKorshuk/comedy-scripts | [
"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
}
}
} | 20 | null | ---
language: en
license: mit
---
# Fairseq-dense 13B - Janeway
## Model Description
Fairseq-dense 13B-Janeway is a finetune created using Fairseq's MoE dense model.
## Training data
The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is identical as dataset used by GPT-Neo-2.7B-Janeway.
Some parts of the dataset have been prepended using the following text: `[Genre: <genre1>,<genre2>]`
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/fairseq-dense-13B-Janeway')
>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
```
### Limitations and Biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
### BibTeX entry and citation info
```
Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts
``` |
AlekseyKorshuk/horror-scripts | [
"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
}
}
} | 19 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/chrismedlandf1/1649255880540/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1252178304192389120/bXT3lbuR_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Chris Medland</div>
<div style="text-align: center; font-size: 14px;">@chrismedlandf1</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Chris Medland.
| Data | Chris Medland |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 196 |
| Short tweets | 102 |
| Tweets kept | 2952 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jton7o0/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @chrismedlandf1's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2qle9s0v) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2qle9s0v/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/chrismedlandf1')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AlekseyKulnevich/Pegasus-HeaderGeneration | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"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:
- vision
- generated_from_trainer
- image-segmentation
datasets:
- segments/sidewalk-semantic
model-index:
- name: sidewalk-semantic-demo
results: []
widget:
- src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg
example_title: Brugge
---
<!-- 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. -->
# sidewalk-semantic-demo
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7591
- Mean Iou: 0.1135
- Mean Accuracy: 0.1608
- Overall Accuracy: 0.6553
- Per Category Iou: [nan, 0.38512238586129177, 0.723869670479682, 3.007496184239216e-05, 0.04329871029371091, 0.0006725029325634934, nan, 0.0, 0.0, 0.0, 0.5420712902837528, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4939727049879936, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5630706428968278, 0.2911849732223226, 0.5899473333836793, 0.0, 0.0, 1.723395088323998e-05, 0.0]
- Per Category Accuracy: [nan, 0.6995968221991989, 0.8870903675336742, 3.007496184239216e-05, 0.043772127605383085, 0.0006731284624713075, nan, 0.0, 0.0, 0.0, 0.8074880705716012, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8257698903048035, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9746918606102934, 0.3057553223999185, 0.6001142624744604, 0.0, 0.0, 1.7275073149137866e-05, 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 2.3589 | 1.0 | 53 | 1.9020 | 0.1014 | 0.1491 | 0.6442 | [0.0, 0.3612513514640175, 0.6751826209974531, 0.0, 0.030376890155720412, 0.0008039971158010613, nan, 2.235273737210043e-05, 0.0, 0.0, 0.5369771616036864, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4924640887729494, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5705205266526164, 0.07944837262494953, 0.5986634961452602, 0.0, 0.0, 0.00011218284533795612, 0.0] | [nan, 0.523053840654786, 0.9469253318772407, 0.0, 0.030589314463641413, 0.0008054985216698098, nan, 2.2371239534454507e-05, 0.0, 0.0, 0.8528562962514211, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.7547252442297603, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9698553453075568, 0.08054302832748386, 0.6107703679316233, 0.0, 0.0, 0.00011444735961303836, 0.0] |
| 2.1214 | 2.0 | 106 | 1.7800 | 0.1158 | 0.1627 | 0.6622 | [nan, 0.3912271306195065, 0.7114203717790301, 0.0001503748092119608, 0.04491329385698775, 0.0008871978593462472, nan, 1.3975654410017748e-06, 0.0, 0.0, 0.5167420849064452, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.49676247687874375, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5965069148571663, 0.3115535309159788, 0.636016670211685, 0.0, 0.0, 0.0, 0.0] | [nan, 0.6306423988442347, 0.9198450793635351, 0.0001503748092119608, 0.045391490029595895, 0.0008886008009872551, nan, 1.3982024709034067e-06, 0.0, 0.0, 0.8587918189550764, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8103648148965297, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9600035488335386, 0.3307256120335472, 0.6505175702762634, 0.0, 0.0, 0.0, 0.0] |
| 1.9022 | 3.0 | 159 | 1.7591 | 0.1135 | 0.1608 | 0.6553 | [nan, 0.38512238586129177, 0.723869670479682, 3.007496184239216e-05, 0.04329871029371091, 0.0006725029325634934, nan, 0.0, 0.0, 0.0, 0.5420712902837528, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4939727049879936, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.5630706428968278, 0.2911849732223226, 0.5899473333836793, 0.0, 0.0, 1.723395088323998e-05, 0.0] | [nan, 0.6995968221991989, 0.8870903675336742, 3.007496184239216e-05, 0.043772127605383085, 0.0006731284624713075, nan, 0.0, 0.0, 0.0, 0.8074880705716012, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8257698903048035, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.9746918606102934, 0.3057553223999185, 0.6001142624744604, 0.0, 0.0, 1.7275073149137866e-05, 0.0] |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AlekseyKulnevich/Pegasus-QuestionGeneration | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"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
}
}
} | 17 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dutch_social
type: dutch_social
args: dutch_social
metrics:
- name: Accuracy
type: accuracy
value: 0.749
- name: F1
type: f1
value: 0.7491844724992662
- name: Precision
type: precision
value: 0.7493911755249737
- name: Recall
type: recall
value: 0.749
---
<!-- 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. -->
# robbert-twitter-sentiment
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6818
- Accuracy: 0.749
- F1: 0.7492
- Precision: 0.7494
- Recall: 0.749
## 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: 5e-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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7485 | 1.0 | 188 | 0.7670 | 0.692 | 0.6915 | 0.6920 | 0.692 |
| 0.5202 | 2.0 | 376 | 0.6818 | 0.749 | 0.7492 | 0.7494 | 0.749 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.12.0
|
AlexMaclean/sentence-compression-roberta | [
"pytorch",
"roberta",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"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
}
}
} | 13 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8651268890789849
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1398
- F1: 0.8651
## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2615 | 1.0 | 525 | 0.1515 | 0.8253 |
| 0.1285 | 2.0 | 1050 | 0.1423 | 0.8490 |
| 0.0803 | 3.0 | 1575 | 0.1398 | 0.8651 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AlexN/xls-r-300m-fr-0 | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"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 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: deberta-base-squad
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. -->
# deberta-base-squad
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the squad 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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 1984
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 2.0
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cpu
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
Alicanke/Wyau | [] | 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:
- all
license: apache-2.0
tags:
- fleurs-lang_id
- google/xtreme_s
- generated_from_trainer
datasets:
- google/xtreme_s
metrics:
- accuracy
model-index:
- name: xtreme_s_xlsr_300m_fleurs_langid
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. -->
# xtreme_s_xlsr_300m_fleurs_langid
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - FLEURS.ALL dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.7271
- Accuracy Af Za: 0.3865
- Accuracy Am Et: 0.8818
- Accuracy Ar Eg: 0.9977
- Accuracy As In: 0.9858
- Accuracy Ast Es: 0.8362
- Accuracy Az Az: 0.8386
- Accuracy Be By: 0.4085
- Accuracy Bn In: 0.9989
- Accuracy Bs Ba: 0.2508
- Accuracy Ca Es: 0.6947
- Accuracy Ceb Ph: 0.9852
- Accuracy Cmn Hans Cn: 0.9799
- Accuracy Cs Cz: 0.5353
- Accuracy Cy Gb: 0.9716
- Accuracy Da Dk: 0.6688
- Accuracy De De: 0.7807
- Accuracy El Gr: 0.7692
- Accuracy En Us: 0.9815
- Accuracy Es 419: 0.9846
- Accuracy Et Ee: 0.5230
- Accuracy Fa Ir: 0.8462
- Accuracy Ff Sn: 0.2348
- Accuracy Fi Fi: 0.9978
- Accuracy Fil Ph: 0.9564
- Accuracy Fr Fr: 0.9852
- Accuracy Ga Ie: 0.8468
- Accuracy Gl Es: 0.5016
- Accuracy Gu In: 0.973
- Accuracy Ha Ng: 0.9163
- Accuracy He Il: 0.8043
- Accuracy Hi In: 0.9354
- Accuracy Hr Hr: 0.3654
- Accuracy Hu Hu: 0.8044
- Accuracy Hy Am: 0.9914
- Accuracy Id Id: 0.9869
- Accuracy Ig Ng: 0.9360
- Accuracy Is Is: 0.0217
- Accuracy It It: 0.8
- Accuracy Ja Jp: 0.7385
- Accuracy Jv Id: 0.5824
- Accuracy Ka Ge: 0.8611
- Accuracy Kam Ke: 0.4184
- Accuracy Kea Cv: 0.8692
- Accuracy Kk Kz: 0.8727
- Accuracy Km Kh: 0.7030
- Accuracy Kn In: 0.9630
- Accuracy Ko Kr: 0.9843
- Accuracy Ku Arab Iq: 0.9577
- Accuracy Ky Kg: 0.8936
- Accuracy Lb Lu: 0.8897
- Accuracy Lg Ug: 0.9253
- Accuracy Ln Cd: 0.9644
- Accuracy Lo La: 0.1580
- Accuracy Lt Lt: 0.4686
- Accuracy Luo Ke: 0.9922
- Accuracy Lv Lv: 0.6498
- Accuracy Mi Nz: 0.9613
- Accuracy Mk Mk: 0.7636
- Accuracy Ml In: 0.6962
- Accuracy Mn Mn: 0.8462
- Accuracy Mr In: 0.3911
- Accuracy Ms My: 0.3632
- Accuracy Mt Mt: 0.6188
- Accuracy My Mm: 0.9705
- Accuracy Nb No: 0.6891
- Accuracy Ne Np: 0.8994
- Accuracy Nl Nl: 0.9093
- Accuracy Nso Za: 0.8873
- Accuracy Ny Mw: 0.4691
- Accuracy Oci Fr: 0.1533
- Accuracy Om Et: 0.9512
- Accuracy Or In: 0.5447
- Accuracy Pa In: 0.8153
- Accuracy Pl Pl: 0.7757
- Accuracy Ps Af: 0.8105
- Accuracy Pt Br: 0.7715
- Accuracy Ro Ro: 0.4122
- Accuracy Ru Ru: 0.9794
- Accuracy Rup Bg: 0.9468
- Accuracy Sd Arab In: 0.5245
- Accuracy Sk Sk: 0.8624
- Accuracy Sl Si: 0.0300
- Accuracy Sn Zw: 0.8843
- Accuracy So So: 0.8803
- Accuracy Sr Rs: 0.0257
- Accuracy Sv Se: 0.0145
- Accuracy Sw Ke: 0.9199
- Accuracy Ta In: 0.9526
- Accuracy Te In: 0.9788
- Accuracy Tg Tj: 0.9883
- Accuracy Th Th: 0.9912
- Accuracy Tr Tr: 0.7887
- Accuracy Uk Ua: 0.0627
- Accuracy Umb Ao: 0.7863
- Accuracy Ur Pk: 0.0134
- Accuracy Uz Uz: 0.4014
- Accuracy Vi Vn: 0.7246
- Accuracy Wo Sn: 0.4555
- Accuracy Xh Za: 1.0
- Accuracy Yo Ng: 0.7353
- Accuracy Yue Hant Hk: 0.7985
- Accuracy Zu Za: 0.4696
- Loss: 1.3789
- Loss Af Za: 2.6778
- Loss Am Et: 0.4615
- Loss Ar Eg: 0.0149
- Loss As In: 0.0764
- Loss Ast Es: 0.4560
- Loss Az Az: 0.5677
- Loss Be By: 1.9231
- Loss Bn In: 0.0024
- Loss Bs Ba: 2.4954
- Loss Ca Es: 1.2632
- Loss Ceb Ph: 0.0426
- Loss Cmn Hans Cn: 0.0650
- Loss Cs Cz: 1.9334
- Loss Cy Gb: 0.1274
- Loss Da Dk: 1.4990
- Loss De De: 0.8820
- Loss El Gr: 0.9839
- Loss En Us: 0.0827
- Loss Es 419: 0.0516
- Loss Et Ee: 1.9264
- Loss Fa Ir: 0.6520
- Loss Ff Sn: 5.4283
- Loss Fi Fi: 0.0109
- Loss Fil Ph: 0.1706
- Loss Fr Fr: 0.0591
- Loss Ga Ie: 0.5174
- Loss Gl Es: 1.2657
- Loss Gu In: 0.0850
- Loss Ha Ng: 0.3234
- Loss He Il: 0.8299
- Loss Hi In: 0.4190
- Loss Hr Hr: 2.9754
- Loss Hu Hu: 0.8345
- Loss Hy Am: 0.0329
- Loss Id Id: 0.0529
- Loss Ig Ng: 0.2523
- Loss Is Is: 6.5153
- Loss It It: 0.8113
- Loss Ja Jp: 1.3968
- Loss Jv Id: 2.0009
- Loss Ka Ge: 0.6162
- Loss Kam Ke: 2.2192
- Loss Kea Cv: 0.5567
- Loss Kk Kz: 0.5592
- Loss Km Kh: 1.7358
- Loss Kn In: 0.1063
- Loss Ko Kr: 0.1519
- Loss Ku Arab Iq: 0.2075
- Loss Ky Kg: 0.4639
- Loss Lb Lu: 0.4454
- Loss Lg Ug: 0.3764
- Loss Ln Cd: 0.1844
- Loss Lo La: 3.8051
- Loss Lt Lt: 2.5054
- Loss Luo Ke: 0.0479
- Loss Lv Lv: 1.3713
- Loss Mi Nz: 0.1390
- Loss Mk Mk: 0.7952
- Loss Ml In: 1.2999
- Loss Mn Mn: 0.7621
- Loss Mr In: 3.7056
- Loss Ms My: 3.0192
- Loss Mt Mt: 1.5520
- Loss My Mm: 0.1514
- Loss Nb No: 1.1194
- Loss Ne Np: 0.4231
- Loss Nl Nl: 0.3291
- Loss Nso Za: 0.5106
- Loss Ny Mw: 2.7346
- Loss Oci Fr: 5.0983
- Loss Om Et: 0.2297
- Loss Or In: 2.5432
- Loss Pa In: 0.7753
- Loss Pl Pl: 0.7309
- Loss Ps Af: 1.0454
- Loss Pt Br: 0.9782
- Loss Ro Ro: 3.5829
- Loss Ru Ru: 0.0598
- Loss Rup Bg: 0.1695
- Loss Sd Arab In: 2.6198
- Loss Sk Sk: 0.5583
- Loss Sl Si: 6.0923
- Loss Sn Zw: 0.4465
- Loss So So: 0.4492
- Loss Sr Rs: 4.7575
- Loss Sv Se: 6.5858
- Loss Sw Ke: 0.4235
- Loss Ta In: 0.1818
- Loss Te In: 0.0808
- Loss Tg Tj: 0.0912
- Loss Th Th: 0.0462
- Loss Tr Tr: 0.7340
- Loss Uk Ua: 4.6777
- Loss Umb Ao: 1.4021
- Loss Ur Pk: 8.4067
- Loss Uz Uz: 4.3297
- Loss Vi Vn: 1.1304
- Loss Wo Sn: 2.2281
- Loss Xh Za: 0.0009
- Loss Yo Ng: 1.3345
- Loss Yue Hant Hk: 1.0728
- Loss Zu Za: 3.7279
- Predict Samples: 77960
## 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: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.5296 | 0.26 | 1000 | 0.4016 | 2.6633 |
| 0.4252 | 0.52 | 2000 | 0.5751 | 1.8582 |
| 0.2989 | 0.78 | 3000 | 0.6332 | 1.6780 |
| 0.3563 | 1.04 | 4000 | 0.6799 | 1.4479 |
| 0.1617 | 1.3 | 5000 | 0.6679 | 1.5066 |
| 0.1409 | 1.56 | 6000 | 0.6992 | 1.4082 |
| 0.01 | 1.82 | 7000 | 0.7071 | 1.2448 |
| 0.0018 | 2.08 | 8000 | 0.7148 | 1.1996 |
| 0.0014 | 2.34 | 9000 | 0.6410 | 1.6505 |
| 0.0188 | 2.6 | 10000 | 0.6840 | 1.4050 |
| 0.0007 | 2.86 | 11000 | 0.6621 | 1.5831 |
| 0.1038 | 3.12 | 12000 | 0.6829 | 1.5441 |
| 0.0003 | 3.38 | 13000 | 0.6900 | 1.3483 |
| 0.0004 | 3.64 | 14000 | 0.6414 | 1.7070 |
| 0.0003 | 3.9 | 15000 | 0.7075 | 1.3198 |
| 0.0002 | 4.16 | 16000 | 0.7105 | 1.3118 |
| 0.0001 | 4.42 | 17000 | 0.7029 | 1.4099 |
| 0.0 | 4.68 | 18000 | 0.7180 | 1.3658 |
| 0.0001 | 4.93 | 19000 | 0.7236 | 1.3514 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.1+cu111
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
|
Alireza-rw/testbot | [] | 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
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: distilbert-base-cased-finetuned-fake-news-detection
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. -->
# distilbert-base-cased-finetuned-fake-news-detection
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0043
- F1: 0.9996
- Accuracy: 0.9996
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| No log | 1.0 | 1684 | 0.0043 | 0.9993 | 0.9993 |
| No log | 2.0 | 3368 | 0.0043 | 0.9996 | 0.9996 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Aliskin/xlm-roberta-base-finetuned-marc | [] | 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 | 2022-04-06T18:40:14Z | ---
license: apache-2.0
tags:
- automatic-speech-recognition
- abdusahmbzuai/arabic_speech_massive_sm
- generated_from_trainer
model-index:
- name: aradia-ctc-distilhubert-ft
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. -->
# aradia-ctc-distilhubert-ft
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the ABDUSAHMBZUAI/ARABIC_SPEECH_MASSIVE_SM - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7114
- Wer: 0.8908
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.43 | 100 | 4.4129 | 1.0 |
| No log | 0.87 | 200 | 3.5927 | 1.0 |
| No log | 1.3 | 300 | 3.3780 | 1.0 |
| No log | 1.74 | 400 | 3.0830 | 1.0 |
| 5.3551 | 2.17 | 500 | 2.6278 | 0.9999 |
| 5.3551 | 2.61 | 600 | 1.8359 | 1.0000 |
| 5.3551 | 3.04 | 700 | 1.7878 | 0.9914 |
| 5.3551 | 3.48 | 800 | 1.5219 | 0.9875 |
| 5.3551 | 3.91 | 900 | 1.4348 | 0.9879 |
| 1.7199 | 4.35 | 1000 | 1.4354 | 0.9644 |
| 1.7199 | 4.78 | 1100 | 1.5210 | 0.9519 |
| 1.7199 | 5.22 | 1200 | 1.3607 | 0.9475 |
| 1.7199 | 5.65 | 1300 | 1.3839 | 0.9343 |
| 1.7199 | 6.09 | 1400 | 1.2806 | 0.8944 |
| 1.2342 | 6.52 | 1500 | 1.3036 | 0.9011 |
| 1.2342 | 6.95 | 1600 | 1.3704 | 0.9072 |
| 1.2342 | 7.39 | 1700 | 1.2981 | 0.8891 |
| 1.2342 | 7.82 | 1800 | 1.2786 | 0.8733 |
| 1.2342 | 8.26 | 1900 | 1.2897 | 0.8867 |
| 0.9831 | 8.69 | 2000 | 1.4436 | 0.8780 |
| 0.9831 | 9.13 | 2100 | 1.3680 | 0.8873 |
| 0.9831 | 9.56 | 2200 | 1.3471 | 0.8692 |
| 0.9831 | 10.0 | 2300 | 1.3725 | 0.8729 |
| 0.9831 | 10.43 | 2400 | 1.4439 | 0.8771 |
| 0.8071 | 10.87 | 2500 | 1.5114 | 0.8928 |
| 0.8071 | 11.3 | 2600 | 1.6156 | 0.8958 |
| 0.8071 | 11.74 | 2700 | 1.4381 | 0.8749 |
| 0.8071 | 12.17 | 2800 | 1.5088 | 0.8717 |
| 0.8071 | 12.61 | 2900 | 1.5486 | 0.8813 |
| 0.6321 | 13.04 | 3000 | 1.4536 | 0.8884 |
| 0.6321 | 13.48 | 3100 | 1.4679 | 0.8947 |
| 0.6321 | 13.91 | 3200 | 1.5628 | 0.9117 |
| 0.6321 | 14.35 | 3300 | 1.5831 | 0.8716 |
| 0.6321 | 14.78 | 3400 | 1.6733 | 0.8702 |
| 0.4998 | 15.22 | 3500 | 1.8225 | 0.8665 |
| 0.4998 | 15.65 | 3600 | 1.8558 | 0.8732 |
| 0.4998 | 16.09 | 3700 | 1.7513 | 0.8766 |
| 0.4998 | 16.52 | 3800 | 1.8562 | 0.8753 |
| 0.4998 | 16.95 | 3900 | 1.9018 | 0.8704 |
| 0.4421 | 17.39 | 4000 | 1.9341 | 0.8789 |
| 0.4421 | 17.82 | 4100 | 1.9582 | 0.8781 |
| 0.4421 | 18.26 | 4200 | 1.8863 | 0.8821 |
| 0.4421 | 18.69 | 4300 | 1.9366 | 0.8847 |
| 0.4421 | 19.13 | 4400 | 2.1902 | 0.8721 |
| 0.3712 | 19.56 | 4500 | 2.1641 | 0.8670 |
| 0.3712 | 20.0 | 4600 | 2.1639 | 0.8776 |
| 0.3712 | 20.43 | 4700 | 2.2695 | 0.9030 |
| 0.3712 | 20.87 | 4800 | 2.1909 | 0.8937 |
| 0.3712 | 21.3 | 4900 | 2.1606 | 0.8959 |
| 0.3067 | 21.74 | 5000 | 2.1756 | 0.8943 |
| 0.3067 | 22.17 | 5100 | 2.4092 | 0.8773 |
| 0.3067 | 22.61 | 5200 | 2.4991 | 0.8721 |
| 0.3067 | 23.04 | 5300 | 2.3340 | 0.8910 |
| 0.3067 | 23.48 | 5400 | 2.3567 | 0.8946 |
| 0.2764 | 23.91 | 5500 | 2.3215 | 0.8897 |
| 0.2764 | 24.35 | 5600 | 2.4824 | 0.9002 |
| 0.2764 | 24.78 | 5700 | 2.4585 | 0.8963 |
| 0.2764 | 25.22 | 5800 | 2.5804 | 0.8879 |
| 0.2764 | 25.65 | 5900 | 2.5814 | 0.8903 |
| 0.2593 | 26.09 | 6000 | 2.5374 | 0.8868 |
| 0.2593 | 26.52 | 6100 | 2.5346 | 0.8922 |
| 0.2593 | 26.95 | 6200 | 2.5465 | 0.8873 |
| 0.2593 | 27.39 | 6300 | 2.6002 | 0.8919 |
| 0.2593 | 27.82 | 6400 | 2.6102 | 0.8928 |
| 0.227 | 28.26 | 6500 | 2.6925 | 0.8914 |
| 0.227 | 28.69 | 6600 | 2.6981 | 0.8913 |
| 0.227 | 29.13 | 6700 | 2.6872 | 0.8891 |
| 0.227 | 29.56 | 6800 | 2.7015 | 0.8897 |
| 0.227 | 30.0 | 6900 | 2.7114 | 0.8908 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
|
Amitabh/doc-classification | [] | 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 | ---
extra_gated_prompt: "You agree to not use the model to conduct experiments that cause harm to human subjects."
extra_gated_fields:
Company: text
Country: text
I agree to use this model for non-commerical use ONLY: checkbox
---
# Temp Model
Hello there what is up! |
Amro-Kamal/gpt | [] | 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 |
---
tags:
- CartPole-v1
---
# **DQN** Agent playing **CartPole-v1**
This is a trained model of a **DQN** agent playing **CartPole-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Evaluation Results
mean_reward=500.00 +/- 0.0
## Usage (with Stable-baselines3)
TODO: Add your code
|
Andranik/TestPytorchClassification | [
"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
}
}
} | 36 | null | ---
language:
- "List of ISO 639-1 code for your language"
- zh
widget:
- text: "中央疫情指揮中心臨時記者會宣布全院區為紅區，擴大隔離，但鄭文燦早在七十二小時前就主張，只要是先前在桃園醫院住院、轉院的患者與陪病家屬，都要居家隔離"
example_title: "範例ㄧ"
- text: "台東地檢署21日指揮警方前往張靜的事務所及黃姓女友所經營的按摩店進行搜索"
example_title: "範例二"
- text: "各地停電事件頻傳，即便經濟部與台電均否認「台灣缺電」，但也難消國人的疑慮。"
example_title: "範例三"
---
---
license: gpl-3.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: albert-base-chinese-0407-ner
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. -->
# albert-base-chinese-0407-ner
This model is a fine-tuned version of [ckiplab/albert-base-chinese](https://huggingface.co/ckiplab/albert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0948
- Precision: 0.8603
- Recall: 0.8871
- F1: 0.8735
- Accuracy: 0.9704
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3484 | 0.05 | 500 | 0.5395 | 0.1841 | 0.1976 | 0.1906 | 0.8465 |
| 0.3948 | 0.09 | 1000 | 0.2910 | 0.6138 | 0.7113 | 0.6590 | 0.9263 |
| 0.2388 | 0.14 | 1500 | 0.2030 | 0.6628 | 0.7797 | 0.7165 | 0.9414 |
| 0.1864 | 0.18 | 2000 | 0.1729 | 0.7490 | 0.7935 | 0.7706 | 0.9498 |
| 0.1754 | 0.23 | 2500 | 0.1641 | 0.7415 | 0.7869 | 0.7635 | 0.9505 |
| 0.1558 | 0.28 | 3000 | 0.1532 | 0.7680 | 0.8002 | 0.7838 | 0.9530 |
| 0.1497 | 0.32 | 3500 | 0.1424 | 0.7865 | 0.8282 | 0.8068 | 0.9555 |
| 0.1488 | 0.37 | 4000 | 0.1373 | 0.7887 | 0.8111 | 0.7997 | 0.9553 |
| 0.1361 | 0.42 | 4500 | 0.1311 | 0.7942 | 0.8382 | 0.8156 | 0.9590 |
| 0.1335 | 0.46 | 5000 | 0.1264 | 0.7948 | 0.8423 | 0.8179 | 0.9596 |
| 0.1296 | 0.51 | 5500 | 0.1242 | 0.8129 | 0.8416 | 0.8270 | 0.9603 |
| 0.1338 | 0.55 | 6000 | 0.1315 | 0.7910 | 0.8588 | 0.8235 | 0.9586 |
| 0.1267 | 0.6 | 6500 | 0.1193 | 0.8092 | 0.8399 | 0.8243 | 0.9609 |
| 0.1207 | 0.65 | 7000 | 0.1205 | 0.8021 | 0.8469 | 0.8239 | 0.9601 |
| 0.1214 | 0.69 | 7500 | 0.1201 | 0.7969 | 0.8489 | 0.8220 | 0.9605 |
| 0.1168 | 0.74 | 8000 | 0.1134 | 0.8087 | 0.8607 | 0.8339 | 0.9620 |
| 0.1162 | 0.78 | 8500 | 0.1127 | 0.8177 | 0.8492 | 0.8331 | 0.9625 |
| 0.1202 | 0.83 | 9000 | 0.1283 | 0.7986 | 0.8550 | 0.8259 | 0.9580 |
| 0.1135 | 0.88 | 9500 | 0.1101 | 0.8213 | 0.8572 | 0.8389 | 0.9638 |
| 0.1121 | 0.92 | 10000 | 0.1097 | 0.8190 | 0.8588 | 0.8384 | 0.9635 |
| 0.1091 | 0.97 | 10500 | 0.1088 | 0.8180 | 0.8521 | 0.8347 | 0.9632 |
| 0.1058 | 1.02 | 11000 | 0.1085 | 0.8136 | 0.8716 | 0.8416 | 0.9630 |
| 0.0919 | 1.06 | 11500 | 0.1079 | 0.8309 | 0.8566 | 0.8436 | 0.9646 |
| 0.0914 | 1.11 | 12000 | 0.1079 | 0.8423 | 0.8542 | 0.8482 | 0.9656 |
| 0.0921 | 1.15 | 12500 | 0.1109 | 0.8312 | 0.8647 | 0.8476 | 0.9646 |
| 0.0926 | 1.2 | 13000 | 0.1240 | 0.8413 | 0.8488 | 0.8451 | 0.9637 |
| 0.0914 | 1.25 | 13500 | 0.1040 | 0.8336 | 0.8666 | 0.8498 | 0.9652 |
| 0.0917 | 1.29 | 14000 | 0.1032 | 0.8352 | 0.8707 | 0.8526 | 0.9662 |
| 0.0928 | 1.34 | 14500 | 0.1052 | 0.8347 | 0.8656 | 0.8498 | 0.9651 |
| 0.0906 | 1.38 | 15000 | 0.1032 | 0.8399 | 0.8619 | 0.8507 | 0.9662 |
| 0.0903 | 1.43 | 15500 | 0.1074 | 0.8180 | 0.8708 | 0.8436 | 0.9651 |
| 0.0889 | 1.48 | 16000 | 0.0990 | 0.8367 | 0.8713 | 0.8537 | 0.9670 |
| 0.0914 | 1.52 | 16500 | 0.1055 | 0.8508 | 0.8506 | 0.8507 | 0.9661 |
| 0.0934 | 1.57 | 17000 | 0.0979 | 0.8326 | 0.8740 | 0.8528 | 0.9669 |
| 0.0898 | 1.62 | 17500 | 0.1022 | 0.8393 | 0.8615 | 0.8502 | 0.9668 |
| 0.0869 | 1.66 | 18000 | 0.0962 | 0.8484 | 0.8762 | 0.8621 | 0.9682 |
| 0.089 | 1.71 | 18500 | 0.1008 | 0.8447 | 0.8714 | 0.8579 | 0.9674 |
| 0.0927 | 1.75 | 19000 | 0.0986 | 0.8379 | 0.8749 | 0.8560 | 0.9673 |
| 0.0883 | 1.8 | 19500 | 0.0965 | 0.8518 | 0.8749 | 0.8632 | 0.9688 |
| 0.0965 | 1.85 | 20000 | 0.0937 | 0.8412 | 0.8766 | 0.8585 | 0.9682 |
| 0.0834 | 1.89 | 20500 | 0.0920 | 0.8451 | 0.8862 | 0.8652 | 0.9687 |
| 0.0817 | 1.94 | 21000 | 0.0943 | 0.8439 | 0.8800 | 0.8616 | 0.9686 |
| 0.088 | 1.99 | 21500 | 0.0927 | 0.8483 | 0.8762 | 0.8620 | 0.9683 |
| 0.0705 | 2.03 | 22000 | 0.0993 | 0.8525 | 0.8783 | 0.8652 | 0.9690 |
| 0.0709 | 2.08 | 22500 | 0.0976 | 0.8610 | 0.8697 | 0.8653 | 0.9689 |
| 0.0655 | 2.12 | 23000 | 0.0997 | 0.8585 | 0.8665 | 0.8625 | 0.9683 |
| 0.0656 | 2.17 | 23500 | 0.0966 | 0.8569 | 0.8822 | 0.8694 | 0.9695 |
| 0.0698 | 2.22 | 24000 | 0.0955 | 0.8604 | 0.8775 | 0.8689 | 0.9696 |
| 0.065 | 2.26 | 24500 | 0.0971 | 0.8614 | 0.8780 | 0.8696 | 0.9697 |
| 0.0653 | 2.31 | 25000 | 0.0959 | 0.8600 | 0.8787 | 0.8692 | 0.9698 |
| 0.0685 | 2.35 | 25500 | 0.1001 | 0.8610 | 0.8710 | 0.8659 | 0.9690 |
| 0.0684 | 2.4 | 26000 | 0.0969 | 0.8490 | 0.8877 | 0.8679 | 0.9690 |
| 0.0657 | 2.45 | 26500 | 0.0954 | 0.8532 | 0.8832 | 0.8680 | 0.9696 |
| 0.0668 | 2.49 | 27000 | 0.0947 | 0.8604 | 0.8793 | 0.8698 | 0.9695 |
| 0.0644 | 2.54 | 27500 | 0.0989 | 0.8527 | 0.8790 | 0.8656 | 0.9696 |
| 0.0685 | 2.59 | 28000 | 0.0955 | 0.8596 | 0.8772 | 0.8683 | 0.9700 |
| 0.0702 | 2.63 | 28500 | 0.0937 | 0.8585 | 0.8837 | 0.8709 | 0.9700 |
| 0.0644 | 2.68 | 29000 | 0.0946 | 0.8605 | 0.8830 | 0.8716 | 0.9702 |
| 0.065 | 2.72 | 29500 | 0.0953 | 0.8617 | 0.8822 | 0.8719 | 0.9701 |
| 0.063 | 2.77 | 30000 | 0.0943 | 0.8597 | 0.8848 | 0.8721 | 0.9701 |
| 0.0638 | 2.82 | 30500 | 0.0941 | 0.8619 | 0.8846 | 0.8731 | 0.9702 |
| 0.066 | 2.86 | 31000 | 0.0942 | 0.8608 | 0.8847 | 0.8726 | 0.9701 |
| 0.0589 | 2.91 | 31500 | 0.0952 | 0.8632 | 0.8836 | 0.8733 | 0.9704 |
| 0.0568 | 2.95 | 32000 | 0.0948 | 0.8603 | 0.8871 | 0.8735 | 0.9704 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
Andranik/TestQA2 | [
"pytorch",
"electra",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"ElectraForQuestionAnswering"
],
"model_type": "electra",
"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: wav2vec2-base-MIR_ST500-demo-colab
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-base-MIR_ST500-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7360
- Wer: 0.9837
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 101.0917 | 16.67 | 100 | 18.8979 | 0.8208 |
| 15.5054 | 33.33 | 200 | 10.9184 | 0.8208 |
| 10.1879 | 50.0 | 300 | 7.6480 | 0.8208 |
| 6.777 | 66.67 | 400 | 3.5386 | 1.0 |
| 3.0546 | 83.33 | 500 | 2.8794 | 1.0 |
| 2.8661 | 100.0 | 600 | 2.8405 | 1.0 |
| 2.847 | 116.67 | 700 | 2.8554 | 1.0 |
| 2.7661 | 133.33 | 800 | 2.6343 | 1.0 |
| 2.3474 | 150.0 | 900 | 2.7464 | 1.0 |
| 2.2464 | 166.67 | 1000 | 2.3565 | 1.0 |
| 2.207 | 183.33 | 1100 | 2.8854 | 1.0 |
| 2.3138 | 200.0 | 1200 | 2.5868 | 1.0 |
| 2.259 | 216.67 | 1300 | 2.6530 | 1.0 |
| 2.1667 | 233.33 | 1400 | 2.4921 | 1.0 |
| 2.1268 | 250.0 | 1500 | 2.5435 | 1.0 |
| 2.1089 | 266.67 | 1600 | 2.5444 | 1.0 |
| 2.0845 | 283.33 | 1700 | 2.6796 | 1.0 |
| 2.0672 | 300.0 | 1800 | 2.5824 | 1.0 |
| 2.055 | 316.67 | 1900 | 2.4631 | 1.0 |
| 2.0317 | 333.33 | 2000 | 2.5751 | 1.0 |
| 2.0141 | 350.0 | 2100 | 2.5627 | 1.0 |
| 1.9914 | 366.67 | 2200 | 2.6132 | 1.0 |
| 1.9489 | 383.33 | 2300 | 2.7527 | 1.0 |
| 1.9146 | 400.0 | 2400 | 2.6121 | 0.9935 |
| 1.893 | 416.67 | 2500 | 2.7110 | 0.9902 |
| 1.845 | 433.33 | 2600 | 2.7410 | 0.9967 |
| 1.8095 | 450.0 | 2700 | 2.7013 | 0.9935 |
| 1.7708 | 466.67 | 2800 | 2.7719 | 0.9935 |
| 1.7224 | 483.33 | 2900 | 2.7740 | 0.9837 |
| 1.6961 | 500.0 | 3000 | 2.7360 | 0.9837 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
AnonymousSub/SR_rule_based_hier_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: anomaly2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# anomaly2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### abnormal
#### normal
 |
AnonymousSub/SR_rule_based_roberta_bert_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 0.8923
- F1: 0.9167
- Precision: 0.8462
- Recall: 1.0
## 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: 5e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0026 | 1.0 | 1956 | 0.0003 | 0.9552 | 0.9636 | 0.9298 | 1.0 |
| 0.0015 | 2.0 | 3912 | 0.0003 | 0.6688 | 0.7815 | 0.6416 | 0.9996 |
| 0.0011 | 3.0 | 5868 | 0.0002 | 0.8923 | 0.9167 | 0.8462 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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
}
}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9299878143347735
- name: Recall
type: recall
value: 0.9391430808815304
- name: F1
type: f1
value: 0.93454302571524
- name: Accuracy
type: accuracy
value: 0.9841453921553053
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0635
- Precision: 0.9300
- Recall: 0.9391
- F1: 0.9345
- Accuracy: 0.9841
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0886 | 1.0 | 1756 | 0.0676 | 0.9198 | 0.9233 | 0.9215 | 0.9809 |
| 0.0382 | 2.0 | 3512 | 0.0605 | 0.9271 | 0.9360 | 0.9315 | 0.9836 |
| 0.0247 | 3.0 | 5268 | 0.0635 | 0.9300 | 0.9391 | 0.9345 | 0.9841 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.9.0
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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
}
}
} | 8 | null | ---
language:
- en
tags:
- text-classification
- fake-news
- pytorch
datasets:
- Fake News https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset
metrics:
- Accuracy, AUC
---
## Model description:
[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.
[Distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) finetuned on the fake news dataset with below Hyperparameters
```
learning rate 5e-5,
batch size 32,
num_train_epochs=2,
```
Full code available @ [DistilBert-FakeNews](https://github.com/anasserhussien/DistilBert-FakeNews)
Dataset available @ [Fake News dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
|
AnonymousSub/SR_rule_based_roberta_twostagequadruplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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
}
}
} | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4718
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.707 | 1.0 | 157 | 2.4883 |
| 2.572 | 2.0 | 314 | 2.4240 |
| 2.5377 | 3.0 | 471 | 2.4355 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_roberta_twostagetriplet_hier_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
"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 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- dutch_social
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: robbert-twitter-sentiment-custom
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: dutch_social
type: dutch_social
args: dutch_social
metrics:
- name: Accuracy
type: accuracy
value: 0.788
- name: F1
type: f1
value: 0.7878005279207152
- name: Precision
type: precision
value: 0.7877102066609215
- name: Recall
type: recall
value: 0.788
---
<!-- 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. -->
# robbert-twitter-sentiment-custom
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6656
- Accuracy: 0.788
- F1: 0.7878
- Precision: 0.7877
- Recall: 0.788
## 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: 5e-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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8287 | 1.0 | 282 | 0.7178 | 0.7007 | 0.6958 | 0.6973 | 0.7007 |
| 0.4339 | 2.0 | 564 | 0.5873 | 0.7667 | 0.7668 | 0.7681 | 0.7667 |
| 0.2045 | 3.0 | 846 | 0.6656 | 0.788 | 0.7878 | 0.7877 | 0.788 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/SR_rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 3 | null | ---
license: cc-by-3.0
---
Architecture: Resnet-18 with two modifications.
1. 1 channel Conv2D as the first layer.
2. 2-way output on FC layer.
Training procedure:
1. Pre-trained in ImageNet.
2. Further training on FashionMNIST.
3. Final training on the task of predicting if Fashion-MNIST images are flipped vertically or not. |
AnonymousSub/SR_rule_based_twostagequadruplet_hier_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 2 | 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
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.919
- name: F1
type: f1
value: 0.9190903538852266
---
<!-- 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: 0.2225
- Accuracy: 0.919
- F1: 0.9191
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.814 | 1.0 | 250 | 0.3153 | 0.904 | 0.9016 |
| 0.2515 | 2.0 | 500 | 0.2225 | 0.919 | 0.9191 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu116
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AnonymousSub/bert-base-uncased_wikiqa | [
"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
}
}
} | 30 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/timjdillon/1649358240896/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1010263656456744960/bXOUw0hb_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Tim Dillon</div>
<div style="text-align: center; font-size: 14px;">@timjdillon</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Tim Dillon.
| Data | Tim Dillon |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 658 |
| Short tweets | 293 |
| Tweets kept | 2289 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1egbnexm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @timjdillon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1yr18emq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1yr18emq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/timjdillon')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 4 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-finetuned-subj_v5_7Epoch
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-german-cased-finetuned-subj_v5_7Epoch
This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3036
- Precision: 0.7983
- Recall: 0.7781
- F1: 0.7881
- Accuracy: 0.9073
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 32 | 0.3438 | 0.6970 | 0.7107 | 0.7038 | 0.8626 |
| No log | 2.0 | 64 | 0.2747 | 0.7688 | 0.7472 | 0.7578 | 0.8902 |
| No log | 3.0 | 96 | 0.2683 | 0.7827 | 0.7893 | 0.7860 | 0.8981 |
| No log | 4.0 | 128 | 0.2768 | 0.8024 | 0.7528 | 0.7768 | 0.9027 |
| No log | 5.0 | 160 | 0.2881 | 0.8102 | 0.7556 | 0.7820 | 0.9060 |
| No log | 6.0 | 192 | 0.3006 | 0.7959 | 0.7669 | 0.7811 | 0.9040 |
| No log | 7.0 | 224 | 0.3036 | 0.7983 | 0.7781 | 0.7881 | 0.9073 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/bert_mean_diff_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 6 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ACTS_feedback1
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. -->
# ACTS_feedback1
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2357
- Accuracy: 0.8936
- Balanced accuracy: 0.8897
- Precision: 0.8951
- Recall: 0.8936
- F1: 0.8915
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:---------:|:------:|:------:|
| 1.0881 | 1.0 | 12 | 1.0513 | 0.5532 | 0.5119 | 0.4004 | 0.5532 | 0.4645 |
| 0.9933 | 2.0 | 24 | 0.9257 | 0.5319 | 0.4952 | 0.3852 | 0.5319 | 0.4463 |
| 0.8065 | 3.0 | 36 | 0.7059 | 0.7234 | 0.7295 | 0.7607 | 0.7234 | 0.7184 |
| 0.5504 | 4.0 | 48 | 0.4259 | 0.8511 | 0.8474 | 0.8486 | 0.8511 | 0.8472 |
| 0.3262 | 5.0 | 60 | 0.3703 | 0.8511 | 0.8654 | 0.8624 | 0.8511 | 0.8499 |
| 0.1877 | 6.0 | 72 | 0.2518 | 0.8723 | 0.8731 | 0.8719 | 0.8723 | 0.8703 |
| 0.1094 | 7.0 | 84 | 0.2283 | 0.9362 | 0.9410 | 0.9415 | 0.9362 | 0.9365 |
| 0.0721 | 8.0 | 96 | 0.2246 | 0.9149 | 0.9244 | 0.9233 | 0.9149 | 0.9149 |
| 0.0521 | 9.0 | 108 | 0.2215 | 0.8936 | 0.8897 | 0.8951 | 0.8936 | 0.8915 |
| 0.0455 | 10.0 | 120 | 0.2357 | 0.8936 | 0.8897 | 0.8951 | 0.8936 | 0.8915 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/bert_mean_diff_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 4 | null | # Description
This model is Part of the NLP assignment for Fatima Fellowship.
This model is a fine-tuned version of 'bert-base-uncased' on the below dataset: [Fake News Dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset).
It achieves the following results on the evaluation set:
- Accuracy: 0.995
- Precision: 0.995
- Recall: 0.995
- F_score: 0.995
# Labels
Fake news: 0
Real news: 1
# Using this model in your code
To use this model, first download it from the hugging face website:
```python
import transformers
from transformers import AutoTokenizer
class Fake_Real_Model_Arch_test(transformers.PreTrainedModel):
def __init__(self,bert):
super(Fake_Real_Model_Arch_test,self).__init__(config=AutoConfig.from_pretrained(MODEL_NAME))
self.bert = bert
num_classes = 2 # number of targets to predict
embedding_dim = 768 # length of embedding dim
self.fc1 = nn.Linear(embedding_dim, num_classes)
self.softmax = nn.Softmax()
def forward(self, text_id, text_mask):
outputs= self.bert(text_id, attention_mask=text_mask)
outputs = outputs[1] # get hidden layers
logit = self.fc1(outputs)
return self.softmax(logit)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = Fake_Real_Model_Arch_test(AutoModel.from_pretrained("rematchka/Bert_fake_news_detection"))
```
|
AnonymousSub/bert_snips | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1010263656456744960/bXOUw0hb_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1468306462245994496/x8koB4rb_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Elon Musk & Tim Dillon & mark normand</div>
<div style="text-align: center; font-size: 14px;">@elonmusk-marknorm-timjdillon</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Elon Musk & Tim Dillon & mark normand.
| Data | Elon Musk | Tim Dillon | mark normand |
| --- | --- | --- | --- |
| Tweets downloaded | 400 | 3240 | 3202 |
| Retweets | 14 | 658 | 116 |
| Short tweets | 117 | 293 | 477 |
| Tweets kept | 269 | 2289 | 2609 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/yk5i85xt/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @elonmusk-marknorm-timjdillon's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zuzgzjdk) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zuzgzjdk/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/elonmusk-marknorm-timjdillon')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AnonymousSub/cline-emanuals-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"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
}
}
} | 27 | null | # DistilBERT with 256k token embeddings
This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs.
Then the model was trained on this dataset with MLM for 1M steps (batch size 64). The token embeddings were updated during MLM.
|
AnonymousSub/cline-emanuals-s10-SR | [] | 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
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- the_pile
---
GPT-NeoX-20B is a 20 billion parameter autoregressive language model trained
on [the Pile](https://pile.eleuther.ai/) using the [GPT-NeoX
library](https://github.com/EleutherAI/gpt-neox). Its architecture intentionally
resembles that of GPT-3, and is almost identical to that of [GPT-J-
6B](https://huggingface.co/EleutherAI/gpt-j-6B). Its training dataset contains
a multitude of English-language texts, reflecting the general-purpose nature
of this model. See the [accompanying paper](https://arxiv.org/abs/2204.06745)
for details about model architecture (including how it differs from GPT-3),
training procedure, and additional evaluations.
### Model details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [GPT-NeoX-20B: An Open-Source Autoregressive Language
Model](https://arxiv.org/abs/2204.06745). For details about the training dataset,
see [the Pile paper](https://arxiv.org/abs/2101.00027), and [its data
sheet](https://arxiv.org/abs/2201.07311).
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing GPT-NeoX-20B documentation before asking about the model
on Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure style="width:30em">
| Hyperparameter | Value |
| ---------------------- | ----------- |
| n<sub>parameters</sub> | 20554567680 |
| n<sub>layers</sub> | 44 |
| d<sub>model</sub> | 6144 |
| n<sub>heads</sub> | 64 |
| d<sub>head</sub> | 96 |
| n<sub>vocab</sub> | 50257 |
| Sequence Length | 2048 |
| Learning Rate | 0.97 x 10<sup>-5</sup> |
| Positional Encoding | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864) |
</figure>
### Uses and limitations
#### Intended use
GPT-NeoX-20B was developed primarily for research purposes. It learns an inner
representation of the English language that can be used to extract features
useful for downstream tasks.
In addition to scientific uses, you may also further fine-tune and adapt
GPT-NeoX-20B for deployment, as long as your use is in accordance with the
Apache 2.0 license. This model works with the [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained GPT-NeoX-20B as a basis for your fine-tuned model, please note that
you need to conduct your own risk and bias assessment.
#### Out-of-scope use
GPT-NeoX-20B is **not** intended for deployment as-is. It is not a product
and cannot be used for human-facing interactions without supervision.
GPT-NeoX-20B has not been fine-tuned for downstream tasks for which language
models are commonly deployed, such as writing genre prose, or commercial
chatbots. This means GPT-NeoX-20B will likely **not** respond to a given prompt
the way products such as ChatGPT do. This is because, unlike GPT-NeoX-20B,
ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human
Feedback (RLHF) to better “understand” human instructions and dialogue.
This model is English-language only, and thus cannot be used for translation
or generating text in other languages.
#### Limitations and biases
The core functionality of GPT-NeoX-20B is to take a string of text and predict
the next token. Remember that the statistically most likely next token need
not result in the most “accurate” text. Never rely on GPT-NeoX-20B to produce
factually accurate output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
GPT-NeoX-20B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
We recommend curating the outputs of this model before presenting it to a human
reader. Please inform your audience that you are using artificially generated
text.
#### How to use
If you simply want to try out some prompts, check out [this
playground](https://20b.eleuther.ai/).
GPT-NeoX-20B can be loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
```
### Training
#### Training dataset
The Pile is a 825GiB general-purpose dataset in English. It was created by
EleutherAI specifically for training large language models. It contains texts
from 22 diverse sources, roughly broken down into five categories: academic
writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project
Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub,
Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for
a breakdown of all data sources, methodology, and a discussion of ethical
implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for
more detailed documentation about the Pile and its component datasets. The
Pile can be downloaded from the [official website](https://pile.eleuther.ai/),
or from a [community mirror](https://the-eye.eu/public/AI/pile/).
The Pile was **not** deduplicated before being used to train GPT-NeoX-20B.
#### Training procedure
GPT-NeoX-20B was trained with a batch size of approximately 3.15M tokens
(1538 sequences of 2048 tokens each), for a total of 150,000 steps. Tensor
parallelism and pipeline parallelism were used to distribute the model across
GPUs. Additional details about the training procedure are in [Section 3 of
the accompanying paper](https://arxiv.org/abs/2204.06745).
### Evaluations
<figure style="width:55em">
| Model | OpenAI’s LAMBADA | SciQ | PIQA | TriviaQA | ARC (Challenge) |
| ------------- | :--------------: | :-----------: | :-----------: | :-----------: | :-------------: |
| GPT-J-6B | 0.683 ± 0.006 | 0.910 ± 0.009 | 0.752 ± 0.010 | 0.170 ± 0.004 | 0.340 ± 0.014 |
| FairSeq 6.7B | 0.673 ± 0.007 | 0.895 ± 0.010 | 0.762 ± 0.010 | 0.221 ± 0.004 | 0.329 ± 0.014 |
| GPT-3 Curie | 0.693 ± 0.006 | 0.918 ± 0.009 | 0.767 ± 0.010 | 0.196 ± 0.004 | 0.334 ± 0.014 |
| FairSeq 13B | 0.709 ± 0.006 | 0.910 ± 0.009 | 0.769 ± 0.010 | 0.270 ± 0.004 | 0.345 ± 0.014 |
| GPT-NeoX-20B | 0.720 ± 0.006 | 0.928 ± 0.008 | 0.779 ± 0.010 | 0.259 ± 0.004 | 0.380 ± 0.014 |
| GPT-3 DaVinci | 0.752 ± 0.006 | 0.949 ± 0.007 | 0.791 ± 0.009 | 0.409 ± 0.005 | 0.435 ± 0.014 |
<figcaption>Zero-shot performance on selected natural language tasks.</figcaption>
</figure>
This is a heavily abridged version of the evaluation results. Appendix D of the
[GPT-NeoX-20B paper](https://arxiv.org/abs/2204.06745) compares more model
sizes, and contains additional evaluations, including on: zero and five-shot
natural language tasks, zero and five-shot Basic Arithmetic and MATH,
and zero-shot Hendrycks tasks.
### BibTeX
To cite the GPT-NeoX-20B paper:
```
@misc{https://doi.org/10.48550/arxiv.2204.06745,
doi = {10.48550/ARXIV.2204.06745},
url = {https://arxiv.org/abs/2204.06745},
author = {Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDonell, Kyle and Phang, Jason and Pieler, Michael and Prashanth, USVSN Sai and Purohit, Shivanshu and Reynolds, Laria and Tow, Jonathan and Wang, Ben and Weinbach, Samuel},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {GPT-NeoX-20B: An Open-Source Autoregressive Language Model},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
AnonymousSub/cline-papers-roberta-0.585 | [
"pytorch",
"roberta",
"transformers"
] | null | {
"architectures": [
"LecbertForPreTraining"
],
"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
}
}
} | 1 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8575809199318569
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1319
- F1: 0.8576
## 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: 5e-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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3264 | 1.0 | 197 | 0.1623 | 0.8139 |
| 0.136 | 2.0 | 394 | 0.1331 | 0.8451 |
| 0.096 | 3.0 | 591 | 0.1319 | 0.8576 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/cline-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"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
}
}
} | 31 | 2022-04-07T21:18:28Z | ---
tags:
- conversational
---
# My Awesome Model of Eva |
AnonymousSub/declutr-emanuals-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"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
}
}
} | 29 | null | python run_squad.py
--model_name_or_path google/canine-s
--do_train
--do_eval
--per_gpu_train_batch_size 1
--per_gpu_eval_batch_size 1
--gradient_accumulation_steps 128
--learning_rate 3e-5
--num_train_epochs 3
--max_seq_length 1024
--doc_stride 128
--max_answer_length 240
--output_dir canine-s-squad
--model_type bert
{
"_name_or_path": "google/canine-s",
"architectures": [
"CanineForQuestionAnswering"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 57344,
"downsampling_rate": 4,
"eos_token_id": 57345,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"local_transformer_stride": 128,
"max_position_embeddings": 16384,
"model_type": "canine",
"num_attention_heads": 12,
"num_hash_buckets": 16384,
"num_hash_functions": 8,
"num_hidden_layers": 12,
"pad_token_id": 0,
"torch_dtype": "float32",
"transformers_version": "4.19.0.dev0",
"type_vocab_size": 16,
"upsampling_kernel_size": 4,
"use_cache": true
}
{'exact': 64.70198675496688, 'f1': 76.57594921776277} |
AnonymousSub/declutr-emanuals-s10-SR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"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
}
}
} | 28 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1317183233495388160/nLbBT6WF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">3bkreno</div>
<div style="text-align: center; font-size: 14px;">@abovethebed</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from 3bkreno.
| Data | 3bkreno |
| --- | --- |
| Tweets downloaded | 484 |
| Retweets | 111 |
| Short tweets | -468 |
| Tweets kept | 841 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/17s3cgho/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @abovethebed's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2al4dbp2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2al4dbp2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/abovethebed')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
AnonymousSub/declutr-model-emanuals | [
"pytorch",
"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
}
}
} | 4 | null | ---
tags:
- conversational
---
# Ron Swanson DialoGPT Model |
AnonymousSub/declutr-s10-AR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"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
}
}
} | 26 | null | ---
tags:
- huggan
- gan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---
# MyModelName
## Model description
Describe the model here (what it does, what it's used for, etc.)
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the model.
If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
## Training procedure
Preprocessing, hardware used, hyperparameters...
## Eval results
## Generated Images
You can embed local or remote images using ``
### BibTeX entry and citation info
```bibtex
@inproceedings{...,
year={2020}
}
``` |
AnonymousSub/declutr-s10-SR | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"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
}
}
} | 36 | 2022-04-08T01:23:57Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1580
- F1: 0.8547
## 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: 5e-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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3718 | 1.0 | 269 | 0.1761 | 0.8223 |
| 0.1535 | 2.0 | 538 | 0.1608 | 0.8404 |
| 0.1074 | 3.0 | 807 | 0.1580 | 0.8547 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/hier_triplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 8 | 2022-04-08T01:49:20Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.7730210016155089
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2928
- F1: 0.7730
## 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: 5e-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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.4548 | 1.0 | 27 | 0.6522 | 0.5457 |
| 0.5214 | 2.0 | 54 | 0.3476 | 0.7404 |
| 0.3186 | 3.0 | 81 | 0.2928 | 0.7730 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/hier_triplet_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 8 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.5793693212185996
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5084
- F1: 0.5794
## 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: 5e-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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7119 | 1.0 | 19 | 1.0009 | 0.2266 |
| 0.891 | 2.0 | 38 | 0.6405 | 0.5281 |
| 0.6023 | 3.0 | 57 | 0.5084 | 0.5794 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
AnonymousSub/rule_based_bert_hier_diff_equal_wts_epochs_1_shard_10 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 6 | null | ---
tags:
- conversational
---
# Harry Potter2 DialoGPT Model |
AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_squad2.0 | [
"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
}
}
} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- accuracy
- f1
model-index:
- name: Bert_Test
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_Test
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1965
- Precision: 0.9332
- Accuracy: 0.9223
- F1: 0.9223
## 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: 32
- 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_steps: 500
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:|
| 0.6717 | 0.4 | 500 | 0.6049 | 0.7711 | 0.6743 | 0.6112 |
| 0.5704 | 0.8 | 1000 | 0.5299 | 0.7664 | 0.7187 | 0.6964 |
| 0.52 | 1.2 | 1500 | 0.4866 | 0.7698 | 0.7537 | 0.7503 |
| 0.4792 | 1.6 | 2000 | 0.4292 | 0.8031 | 0.793 | 0.7927 |
| 0.4332 | 2.0 | 2500 | 0.3920 | 0.8318 | 0.8203 | 0.8198 |
| 0.381 | 2.4 | 3000 | 0.3723 | 0.9023 | 0.8267 | 0.8113 |
| 0.3625 | 2.8 | 3500 | 0.3134 | 0.8736 | 0.8607 | 0.8601 |
| 0.3325 | 3.2 | 4000 | 0.2924 | 0.8973 | 0.871 | 0.8683 |
| 0.3069 | 3.6 | 4500 | 0.2671 | 0.8916 | 0.8847 | 0.8851 |
| 0.2866 | 4.0 | 5000 | 0.2571 | 0.8920 | 0.8913 | 0.8926 |
| 0.2595 | 4.4 | 5500 | 0.2450 | 0.8980 | 0.9 | 0.9015 |
| 0.2567 | 4.8 | 6000 | 0.2246 | 0.9057 | 0.9043 | 0.9054 |
| 0.2255 | 5.2 | 6500 | 0.2263 | 0.9332 | 0.905 | 0.9030 |
| 0.2237 | 5.6 | 7000 | 0.2083 | 0.9265 | 0.9157 | 0.9156 |
| 0.2248 | 6.0 | 7500 | 0.2039 | 0.9387 | 0.9193 | 0.9185 |
| 0.2086 | 6.4 | 8000 | 0.2038 | 0.9436 | 0.9193 | 0.9181 |
| 0.2029 | 6.8 | 8500 | 0.1965 | 0.9332 | 0.9223 | 0.9223 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
AnonymousSub/rule_based_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"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
}
}
} | 6 | null | ---
language:
- es
tags:
- biomedical
- clinical
- ehr
- spanish
license: apache-2.0
metrics:
- ppl
widget:
- text: "El único antecedente personal a reseñar era la <mask> arterial."
- text: "Las radiologías óseas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."
- text: "En el <mask> toraco-abdómino-pélvico no se encontraron hallazgos patológicos de interés."
---
# Biomedical-clinical language model for Spanish
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Evaluation](#evaluation)
- [Additional information](#additional-information)
- [Author](#author)
- [Contact information](#contact-information)
- [Copyright](#copyright)
- [Licensing information](#licensing-information)
- [Funding](#funding)
- [Citing information](#citing-information)
- [Disclaimer](#disclaimer)
</details>
## Model description
Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).
## Intended uses and limitations
The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section). However, it is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification.
## How to use
## Limitations and bias
At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
## Training
### Tokenization and model pretraining
This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a
**biomedical-clinical** corpus in Spanish collected from several sources (see next section).
The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2)
used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.
### Training corpora and preprocessing
The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are:
- data parsing in different formats
- sentence splitting
- language detection
- filtering of ill-formed sentences
- deduplication of repetitive contents
- keep the original document boundaries
Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora has been applied.
Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of more than 1B tokens. The table below shows some basic statistics of the individual cleaned corpora:
| Name | No. tokens | Description |
|-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Medical crawler](https://zenodo.org/record/4561970) | 903,558,13 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. |
| Clinical cases misc. | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. |
| EHR documents | 95,267,20 | Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens. |
| [Scielo](https://zenodo.org/record/2541681#.YlP1DshBwio) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. |
| [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442 | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. |
| Wikipedia_life_sciences | 13,890,501 | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. |
| Patents | 13,463,387 | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". |
| [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip) | 5,377,448 | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. |
| [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR) | 4,166,077 | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources is aggregated from the MedlinePlus source. |
| PubMed | 1,858,966 | Open-access articles from the PubMed repository crawled in 2017. |
## Evaluation
The model has been fine-tuned on three Named Entity Recognition (NER) tasks using three clinical NER datasets:
- [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).
- [CANTEMIST](https://zenodo.org/record/3978041#.YTt5qH2xXbQ): is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ).
- ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables.
We addressed the NER task as a token classification problem using a standard linear layer along with the BIO tagging schema. We compared our models with the general-domain Spanish [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne), the general-domain multilingual model that supports Spanish [mBERT](https://huggingface.co/bert-base-multilingual-cased), the domain-specific English model [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2), and three domain-specific models based on continual pre-training, [mBERT-Galén](https://ieeexplore.ieee.org/document/9430499), [XLM-R-Galén](https://ieeexplore.ieee.org/document/9430499) and [BETO-Galén](https://ieeexplore.ieee.org/document/9430499).
The table below shows the F1 scores obtained:
| Tasks/Models | bsc-bio-ehr-es | XLM-R-Galén | BETO-Galén | mBERT-Galén | mBERT | BioBERT | roberta-base-bne |
|--------------|----------------|--------------------|--------------|--------------|--------------|--------------|------------------|
| PharmaCoNER | **0.8913** | 0.8754 | 0.8537 | 0.8594 | 0.8671 | 0.8545 | 0.8474 |
| CANTEMIST | **0.8340** | 0.8078 | 0.8153 | 0.8168 | 0.8116 | 0.8070 | 0.7875 |
| ICTUSnet | **0.8756** | 0.8716 | 0.8498 | 0.8509 | 0.8631 | 0.8521 | 0.8677 |
The fine-tuning scripts can be found in the official GitHub [repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).
## Additional information
### Author
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
### Contact information
For further information, send an email to <[email protected]>
### Copyright
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Licensing information
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
### Citing information
If you use these models, please cite our work:
```bibtext
@inproceedings{carrino-etal-2022-pretrained,
title = "Pretrained Biomedical Language Models for Clinical {NLP} in {S}panish",
author = "Carrino, Casimiro Pio and
Llop, Joan and
P{\`a}mies, Marc and
Guti{\'e}rrez-Fandi{\~n}o, Asier and
Armengol-Estap{\'e}, Jordi and
Silveira-Ocampo, Joaqu{\'\i}n and
Valencia, Alfonso and
Gonzalez-Agirre, Aitor and
Villegas, Marta",
booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.bionlp-1.19",
doi = "10.18653/v1/2022.bionlp-1.19",
pages = "193--199",
abstract = "This work presents the first large-scale biomedical Spanish language models trained from scratch, using large biomedical corpora consisting of a total of 1.1B tokens and an EHR corpus of 95M tokens. We compared them against general-domain and other domain-specific models for Spanish on three clinical NER tasks. As main results, our models are superior across the NER tasks, rendering them more convenient for clinical NLP applications. Furthermore, our findings indicate that when enough data is available, pre-training from scratch is better than continual pre-training when tested on clinical tasks, raising an exciting research question about which approach is optimal. Our models and fine-tuning scripts are publicly available at HuggingFace and GitHub.",
}
```
### Disclaimer
<details>
<summary>Click to expand</summary>
The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
</details>
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.