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
File size: 362 Bytes
4ddf440 2d07a23 4ddf440 | 1 2 3 4 5 6 7 8 9 | 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 |