Instructions to use SaulLu/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaulLu/test-model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("SaulLu/test-model") model = AutoModelForPreTraining.from_pretrained("SaulLu/test-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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| # sahajBERT News Category Classification | |
| ## Model description | |
| You can embed local or remote images using `` | |
| ## Intended uses & limitations | |
| #### How to use | |
| ```python | |
| # You can include sample code which will be formatted | |
| ``` | |
| #### Limitations and bias | |
| Provide examples of latent issues and potential remediations. | |
| ## Training data | |
| Describe the data you used to train the model. | |
| If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. | |
| ## Training procedure | |
| ### Collaborative training procedure | |
| [here](https://huggingface.co/albertvillanova) | |
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| Preprocessing, hardware used, hyperparameters... | |
| ## Eval results | |
| ### BibTeX entry and citation info | |
| ```bibtex | |
| @inproceedings{..., | |
| year={2020} | |
| } | |
| ``` |