Datasets:

---

ftopal

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huggingface-models

like 0

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "da", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "da" ]

[ "# HPLT Bert for Danish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #da #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Danish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "de", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "de" ]

[ "# HPLT Bert for German\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #de #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for German\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "el", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "el" ]

[ "# HPLT Bert for Greek\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #el #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Greek\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "gguf", "license:apache-2.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#gguf #license-apache-2.0 #region-us \n" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "eo", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "eo" ]

[ "# HPLT Bert for Esperanto\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #eo #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Esperanto\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# ppo_zephyr1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-06\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ppo_zephyr1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-06\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "es", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "es" ]

[ "# HPLT Bert for Spanish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #es #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Spanish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "et", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "et" ]

[ "# HPLT Bert for Estonian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #et #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Estonian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "eu", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "eu" ]

[ "# HPLT Bert for Basque\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #eu #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Basque\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

[]

[]

[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "fa", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "fa" ]

[ "# HPLT Bert for Persian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #fa #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Persian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "fi", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "fi" ]

[ "# HPLT Bert for Finnish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #fi #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Finnish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "en", "license:other", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#gguf #facebook #meta #pytorch #llama #llama-3 #text-generation #en #license-other #region-us \n" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "fr", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "fr" ]

[ "# HPLT Bert for French\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #fr #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for French\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ga", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ga" ]

[ "# HPLT Bert for Irish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ga #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Irish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "en", "license:other", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#gguf #facebook #meta #pytorch #llama #llama-3 #text-generation #en #license-other #region-us \n" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "gl", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "gl" ]

[ "# HPLT Bert for Galician\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #gl #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Galician\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "gu", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "gu" ]

[ "# HPLT Bert for Gujarati\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #gu #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Gujarati\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "hbs", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "hbs" ]

[ "# HPLT Bert for Serbo-Croatian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #hbs #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Serbo-Croatian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "license:cc-by-nc-4.0", "region:us" ]

[]

[]

[ "# Llama-3-8B-Instruct-DADA-exl2-rpcal\n\nQuantized using 200 samples of 8192 tokens from an RP-oriented PIPPA dataset.\n\nBranches:\n- 'main' -- 'URL'\n- '8b8h' -- 8bpw, 8bit lm_head\n- '6b6h' -- 6bpw, 6bit lm_head\n- '4b6h' -- 4bpw, 6bit lm_head\n\nOriginal model link: Envoid/Llama-3-8B-Instruct-DADA\n\nOriginal model README below.\n\n-----", "## Llama-3-8B-Instruct-DADA\n![](URL", "# Warning: This model is experimental and thus potentially unpredictable. \n\nThis model employs the same strategy as Mixtral Instruct ITR DADA\n\nI trained Llama-3-8B-Instruct on the Alpaca-DADA dataset for 10 epochs at 1e-6 learning rate.\nI then did a 50/50 SLERP merge of the resulting model back onto Llama-3-8B-Instruct\n\nThis model may require custom stopping strings to tame due to current issues surrounding Llama-3 EOS tokens and various back-ends. \nIt certainly gives some interesting answers using an assistant template/card in SillyTavern, though. \n\nThe below answer is one of the more interesting answers I've gotten out of an LLM on the same query, although there was an indentiation error (indicated by the red circle)\n![](URL\n\nTraining was done using qlora-pipe\n\nGGUFs care of Quant Cartel" ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "# Llama-3-8B-Instruct-DADA-exl2-rpcal\n\nQuantized using 200 samples of 8192 tokens from an RP-oriented PIPPA dataset.\n\nBranches:\n- 'main' -- 'URL'\n- '8b8h' -- 8bpw, 8bit lm_head\n- '6b6h' -- 6bpw, 6bit lm_head\n- '4b6h' -- 4bpw, 6bit lm_head\n\nOriginal model link: Envoid/Llama-3-8B-Instruct-DADA\n\nOriginal model README below.\n\n-----", "## Llama-3-8B-Instruct-DADA\n![](URL", "# Warning: This model is experimental and thus potentially unpredictable. \n\nThis model employs the same strategy as Mixtral Instruct ITR DADA\n\nI trained Llama-3-8B-Instruct on the Alpaca-DADA dataset for 10 epochs at 1e-6 learning rate.\nI then did a 50/50 SLERP merge of the resulting model back onto Llama-3-8B-Instruct\n\nThis model may require custom stopping strings to tame due to current issues surrounding Llama-3 EOS tokens and various back-ends. \nIt certainly gives some interesting answers using an assistant template/card in SillyTavern, though. \n\nThe below answer is one of the more interesting answers I've gotten out of an LLM on the same query, although there was an indentiation error (indicated by the red circle)\n![](URL\n\nTraining was done using qlora-pipe\n\nGGUFs care of Quant Cartel" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "he", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "he" ]

[ "# HPLT Bert for Hebrew\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #he #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Hebrew\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "hi", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "hi" ]

[ "# HPLT Bert for Hindi\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #hi #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Hindi\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "hu", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "hu" ]

[ "# HPLT Bert for Hungarian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #hu #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Hungarian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "gguf", "text-generation", "dataset:mlabonne/orpo-dpo-mix-40k", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:jondurbin/airoboros-3.2", "dataset:microsoft/orca-math-word-problems-200k", "dataset:m-a-p/Code-Feedback", "dataset:MaziyarPanahi/WizardLM_evol_instruct_V2_196k", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #gguf #text-generation #dataset-mlabonne/orpo-dpo-mix-40k #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #base_model-meta-llama/Meta-Llama-3-8B #license-other #endpoints_compatible #region-us \n" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "hy", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "hy" ]

[ "# HPLT Bert for Armenian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #hy #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Armenian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "id", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "id" ]

[ "# HPLT Bert for Indonesian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #id #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Indonesian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "is", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "is" ]

[ "# HPLT Bert for Icelandic\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #is #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Icelandic\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "it", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "it" ]

[ "# HPLT Bert for Italian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #it #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Italian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ja", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ja" ]

[ "# HPLT Bert for Japanese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ja #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Japanese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ka", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ka" ]

[ "# HPLT Bert for Georgian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ka #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Georgian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "safetensors", "internvl_chat", "feature-extraction", "visual-question-answering", "custom_code", "dataset:laion/laion2B-en", "dataset:laion/laion-coco", "dataset:laion/laion2B-multi", "dataset:kakaobrain/coyo-700m", "dataset:conceptual_captions", "dataset:wanng/wukong100m", "arxiv:2312.14238", "license:mit", "region:us" ]

[ "2312.14238" ]

[]

[]

[ "TAGS\n#transformers #safetensors #internvl_chat #feature-extraction #visual-question-answering #custom_code #dataset-laion/laion2B-en #dataset-laion/laion-coco #dataset-laion/laion2B-multi #dataset-kakaobrain/coyo-700m #dataset-conceptual_captions #dataset-wanng/wukong100m #arxiv-2312.14238 #license-mit #region-us \n" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "kk", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "kk" ]

[ "# HPLT Bert for Kazakh\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #kk #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Kazakh\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "kn", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "kn" ]

[ "# HPLT Bert for Kannada\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #kn #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Kannada\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ko", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ko" ]

[ "# HPLT Bert for Korean\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ko #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Korean\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

[]

[]

[ "# outputs\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n", "# outputs\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "Roleplay", "roleplay", "en", "base_model:Sao10K/Fimbulvetr-11B-v2", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# About this model\n\nThis model can handle (limited) TSF content. If you Character Card have complex plot, maybe you should try other model (maybe bigger parameter?).\n\n- Another version of main model, which have lora rank is 128 to reduce underfitting.\n\nDo you know TSF, TS, TG? A lot of model don't really know about that, so I do some experiment to finetune TSF dataset.\n\n- Finetuned with Chinese Novels dataset, to increase the accuracy in TSF theme, which is not quite popular.\n (R18 dataset). You should include chinese/japanese word about tag you want(search it in pixiv) in your character card to trigger it.\n This finetune idea is suitable for Chinese Roleplay than English (Becaue I could only find good Chinese datasets about it , it is nice that if you can open a discussion about English TSF datasets). But it still affect the models writing styles, so maybe less GPT-like response in both Chinese and English?.\n- Finetuned from model : Sao10K/Fimbulvetr-11B-v2 . Thank Sao10K a lot :)", "## 8k Context Length BTW, the original Fimbulvetr and Solar have only 4k context length, so I extended it .", "## GGUF version? here is it.", "## Dataset\nAll chinese novels dataset", "# Thank Unsloth for good finetuning tool. This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #Roleplay #roleplay #en #base_model-Sao10K/Fimbulvetr-11B-v2 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# About this model\n\nThis model can handle (limited) TSF content. If you Character Card have complex plot, maybe you should try other model (maybe bigger parameter?).\n\n- Another version of main model, which have lora rank is 128 to reduce underfitting.\n\nDo you know TSF, TS, TG? A lot of model don't really know about that, so I do some experiment to finetune TSF dataset.\n\n- Finetuned with Chinese Novels dataset, to increase the accuracy in TSF theme, which is not quite popular.\n (R18 dataset). You should include chinese/japanese word about tag you want(search it in pixiv) in your character card to trigger it.\n This finetune idea is suitable for Chinese Roleplay than English (Becaue I could only find good Chinese datasets about it , it is nice that if you can open a discussion about English TSF datasets). But it still affect the models writing styles, so maybe less GPT-like response in both Chinese and English?.\n- Finetuned from model : Sao10K/Fimbulvetr-11B-v2 . Thank Sao10K a lot :)", "## 8k Context Length BTW, the original Fimbulvetr and Solar have only 4k context length, so I extended it .", "## GGUF version? here is it.", "## Dataset\nAll chinese novels dataset", "# Thank Unsloth for good finetuning tool. This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ky", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ky" ]

[ "# HPLT Bert for Kyrgyz\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ky #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Kyrgyz\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "la", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "la" ]

[ "# HPLT Bert for Latin\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #la #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Latin\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "lt", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "lt" ]

[ "# HPLT Bert for Lithuanian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #lt #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Lithuanian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "lv", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "lv" ]

[ "# HPLT Bert for Latvian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #lv #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Latvian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "mk", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "mk" ]

[ "# HPLT Bert for Macedonian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #mk #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Macedonian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ml", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ml" ]

[ "# HPLT Bert for Malayalam\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ml #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Malayalam\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "onnx", "text-to-image", "region:us" ]

[]

[]

[ "# SDXL Lightning - Onnx Olive DirectML Optimized", "## Original Model\nURL", "## C# Inference Demo\nURL", "## Inference Result\n!Intro Image" ]

[ "TAGS\n#onnx #text-to-image #region-us \n", "# SDXL Lightning - Onnx Olive DirectML Optimized", "## Original Model\nURL", "## C# Inference Demo\nURL", "## Inference Result\n!Intro Image" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "mn", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "mn" ]

[ "# HPLT Bert for Mongolian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #mn #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Mongolian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "mr", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "mr" ]

[ "# HPLT Bert for Marathi\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #mr #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Marathi\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ms", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ms" ]

[ "# HPLT Bert for Malay\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ms #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Malay\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "mt", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "mt" ]

[ "# HPLT Bert for Maltese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #mt #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Maltese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "my", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "my" ]

[ "# HPLT Bert for Burmese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #my #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Burmese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "nb", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "nb" ]

[ "# HPLT Bert for Norwegian Bokmål\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #nb #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Norwegian Bokmål\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ne", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ne" ]

[ "# HPLT Bert for Nepali\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ne #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Nepali\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "nl", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "nl" ]

[ "# HPLT Bert for Dutch\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #nl #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Dutch\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "nn", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "nn" ]

[ "# HPLT Bert for Norwegian Nynorsk\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #nn #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Norwegian Nynorsk\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "pa", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "pa" ]

[ "# HPLT Bert for Panjabi\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #pa #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Panjabi\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "pl", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "pl" ]

[ "# HPLT Bert for Polish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #pl #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Polish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ps", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ps" ]

[ "# HPLT Bert for Pushto\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ps #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Pushto\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "peft", "tensorboard", "safetensors", "mistral", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "4-bit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.40.0\n* Pytorch 2.1.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #mistral #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.40.0\n* Pytorch 2.1.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 50", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_steps: 0.03\n* training\\_steps: 50", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "pt", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "pt" ]

[ "# HPLT Bert for Portuguese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #pt #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Portuguese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:bertin-project/bertin-roberta-base-spanish", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-bertin-project/bertin-roberta-base-spanish #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ro", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ro" ]

[ "# HPLT Bert for Romanian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ro #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Romanian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ru", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ru" ]

[ "# HPLT Bert for Russian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ru #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Russian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "si", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "si" ]

[ "# HPLT Bert for Sinhala\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #si #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Sinhala\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "stable-baselines3", "BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

[]

[]

[ "# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "sk", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "sk" ]

[ "# HPLT Bert for Slovak\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #sk #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Slovak\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "sl", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "sl" ]

[ "# HPLT Bert for Slovenian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #sl #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Slovenian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "so", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "so" ]

[ "# HPLT Bert for Somali\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #so #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Somali\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "sq", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "sq" ]

[ "# HPLT Bert for Albanian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #sq #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Albanian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "sv", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "sv" ]

[ "# HPLT Bert for Swedish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #sv #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Swedish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "sw", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "sw" ]

[ "# HPLT Bert for Swahili\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #sw #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Swahili\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ta", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ta" ]

[ "# HPLT Bert for Tamil\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ta #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Tamil\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "te", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "te" ]

[ "# HPLT Bert for Telugu\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #te #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Telugu\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "th", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "th" ]

[ "# HPLT Bert for Thai\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #th #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Thai\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "tl", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "tl" ]

[ "# HPLT Bert for Tagalog\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #tl #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Tagalog\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "tr", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "tr" ]

[ "# HPLT Bert for Turkish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #tr #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Turkish\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "tt", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "tt" ]

[ "# HPLT Bert for Tatar\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #tt #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Tatar\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:bigcode/starcoder2-7b", "license:bigcode-openrail-m", "region:us" ]

[]

[]

[ "# finetune_starcoder2\n\nThis model is a fine-tuned version of bigcode/starcoder2-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 0\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 20\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.0\n- Pytorch 2.1.2+git98a6632\n- Datasets 2.17.1\n- Tokenizers 0.19.1" ]

[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-bigcode/starcoder2-7b #license-bigcode-openrail-m #region-us \n", "# finetune_starcoder2\n\nThis model is a fine-tuned version of bigcode/starcoder2-7b on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 0\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 8\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 20\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.40.0\n- Pytorch 2.1.2+git98a6632\n- Datasets 2.17.1\n- Tokenizers 0.19.1" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "uk", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "uk" ]

[ "# HPLT Bert for Ukrainian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #uk #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Ukrainian\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "ur", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "ur" ]

[ "# HPLT Bert for Urdu\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #ur #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Urdu\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "uz", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "uz" ]

[ "# HPLT Bert for Uzbek\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #uz #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Uzbek\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "vi", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "vi" ]

[ "# HPLT Bert for Vietnamese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #vi #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Vietnamese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "transformers", "pytorch", "fill-mask", "BERT", "HPLT", "encoder", "custom_code", "zh", "dataset:HPLT/hplt_monolingual_v1_2", "arxiv:2403.14009", "license:apache-2.0", "autotrain_compatible", "region:us" ]

[ "2403.14009" ]

[ "zh" ]

[ "# HPLT Bert for Chinese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "TAGS\n#transformers #pytorch #fill-mask #BERT #HPLT #encoder #custom_code #zh #dataset-HPLT/hplt_monolingual_v1_2 #arxiv-2403.14009 #license-apache-2.0 #autotrain_compatible #region-us \n", "# HPLT Bert for Chinese\n\n<img src=\"URL width=12.5%>\n\nThis is one of the encoder-only monolingual language models trained as a first release by the HPLT project.\nIt is a so called masked language models. In particular, we used the modification of the classic BERT model named LTG-BERT.\n\nA monolingual LTG-BERT model is trained for every major language in the HPLT 1.2 data release (*75* models total).\n\nAll the HPLT encoder-only models use the same hyper-parameters, roughly following the BERT-base setup:\n- hidden size: 768\n- attention heads: 12\n- layers: 12\n- vocabulary size: 32768\n\nEvery model uses its own tokenizer trained on language-specific HPLT data. \nSee sizes of the training corpora, evaluation results and more in our language model training report.\n\nThe training code.\n\nThe training statistics of all 75 runs", "## Example usage\n\nThis model currently needs a custom wrapper from 'modeling_ltgbert.py', you should therefore load the model with 'trust_remote_code=True'.\n\n\n\nThe following classes are currently implemented: 'AutoModel', 'AutoModelMaskedLM', 'AutoModelForSequenceClassification', 'AutoModelForTokenClassification', 'AutoModelForQuestionAnswering' and 'AutoModeltForMultipleChoice'.", "## Cite us" ]

[ "instruction tuning", "chemistry", "molecule", "small molecule", "en", "license:cc-by-4.0", "region:us" ]

[]

[ "en" ]

[ "## ️ Usage\n\nFor instructions to run the model, please refer to our repository.", "## Limitations\n\nWhile the model is carefully trained, we do not guarantee its effectiveness. The model may output incorrect or inaccurate information. Please use it at your own risk.\n\nAdditionally, the model is built as a mature product but solely for research purpose. It may generate harmful or biased information. We emphatically urge all users to adhere to the highest ethical standards when using the model, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

[ "TAGS\n#instruction tuning #chemistry #molecule #small molecule #en #license-cc-by-4.0 #region-us \n", "## ️ Usage\n\nFor instructions to run the model, please refer to our repository.", "## Limitations\n\nWhile the model is carefully trained, we do not guarantee its effectiveness. The model may output incorrect or inaccurate information. Please use it at your own risk.\n\nAdditionally, the model is built as a mature product but solely for research purpose. It may generate harmful or biased information. We emphatically urge all users to adhere to the highest ethical standards when using the model, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

[ "gguf", "pruna-ai", "region:us" ]

[]

[]

[ "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code\n\n```\n\n* Option D - Running with LangChain\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nConfigurations\n--------------\n\n\nThe configuration info are in 'smash\\_config.json'.\n\n\nCredits & License\n-----------------\n\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.\n\n\nWant to compress other models?\n------------------------------\n\n\n* Contact us and tell us which model to compress next here.\n* Request access to easily compress your own AI models here." ]

[ "TAGS\n#gguf #pruna-ai #region-us \n", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code\n\n```\n\n* Option D - Running with LangChain\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nConfigurations\n--------------\n\n\nThe configuration info are in 'smash\\_config.json'.\n\n\nCredits & License\n-----------------\n\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.\n\n\nWant to compress other models?\n------------------------------\n\n\n* Contact us and tell us which model to compress next here.\n* Request access to easily compress your own AI models here." ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]