This is a BERT language model pre-trained on ~3 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a DistilBERT language model pre-trained on ~6 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a RoBERTa language model pre-trained on ~6 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a XLMRoBERTa language model pre-trained on ~3 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a BERT language model pre-trained on ~3 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a DistilBERT language model pre-trained on ~10 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a RoBERTa language model pre-trained on ~10 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a XLMRoBERTa language model pre-trained on ~3 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a BERT language model pre-trained on ~1.6 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a DistilBERT language model pre-trained on ~2 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a RoBERTa language model pre-trained on ~2 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
This is a XLMRoBERTa language model pre-trained on ~1.6 GB of monolingual training corpus. The pre-training data was majorly taken from [OSCAR](https://oscar-corpus.com/).
This model can be fine-tuned on various downstream tasks like text-classification, POS-tagging, question-answering, etc. Embeddings from this model can also be used for feature-based training.
The original language model has been trained using `PyTorch` and hence the use of `pytorch_model.bin` weights file is recommended. The h5 file for `Tensorflow` has been generated manually by commands suggested [here](https://huggingface.co/transformers/model_sharing.html).
