Instructions to use hsuvaskakoty/bart_temp_dm_10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsuvaskakoty/bart_temp_dm_10k with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hsuvaskakoty/bart_temp_dm_10k") model = AutoModelForSeq2SeqLM.from_pretrained("hsuvaskakoty/bart_temp_dm_10k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from hsuvaskakoty/bart_temp_dm_10k: direct link, hf CLI and curl.
- Browser
- Download file 362 Bytes
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https://huggingface.co/hsuvaskakoty/bart_temp_dm_10k/resolve/main/README.md
- Command line
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hf download hf://hsuvaskakoty/bart_temp_dm_10k/README.md
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curl -L -o README.md https://huggingface.co/hsuvaskakoty/bart_temp_dm_10k/resolve/main/README.md
362 Bytes
| A BART-base model fine-tuned for temporal definition modelling task. The dataset comprises 10000 definition-context pairs and is organised in the following way. | |
| Definition: \<t\> Coronavirus \<t\> is a type of virus. | |
| Context :\<y\> 2022 \</y\> This year \<t\> Coronavirus \<t\> were very prudent in many countries. | |
| The validation loss for the model is: 0.88 | |