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:
# 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") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
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
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