Instructions to use amir22010/PyABSA_Hospital_Multilingual_allenai_tk-instruct-base-def-pos_FinedTuned_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amir22010/PyABSA_Hospital_Multilingual_allenai_tk-instruct-base-def-pos_FinedTuned_Model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("amir22010/PyABSA_Hospital_Multilingual_allenai_tk-instruct-base-def-pos_FinedTuned_Model") model = AutoModelForSeq2SeqLM.from_pretrained("amir22010/PyABSA_Hospital_Multilingual_allenai_tk-instruct-base-def-pos_FinedTuned_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: cc-by-nc-4.0 | |
| [allenai/tk-instruct-base-def-pos](https://huggingface.co/allenai/tk-instruct-base-def-pos) model fined tuned on custom pyabsa english model from 'ATEPC_MULTILINGUAL_CHECKPOINT' auto annotated hospital reviews [keywords,polarity] dataset. | |
| Training Results: | |
| Epoch | Training Loss | Validation Loss | |
| 1 | 0.061900 | 0.047395 | |
| 2 | 0.038300 | 0.035213 | |
| 3 | 0.029700 | 0.028486 | |
| 4 | 0.024900 | 0.028562 | |
| ----------------------------------------------------------------------------------------------------------------------------------------------- | |
| Evaluation Results: | |
| Train Precision: 0.9682688696958318 | |
| Train Recall: 0.9692090837901332 | |
| Train F1: 0.968738748610698 | |
| Test Precision: 0.965 | |
| Test Recall: 0.9620517768129925 | |
| Test F1: 0.9635236331427651 | |
| UnseenTest Precision: 0.9670502092050209 | |
| UnseenTest Recall: 0.9685699319015191 | |
| UnseenTest F1: 0.9678094739596965 |