Instructions to use HooshvareLab/bert-base-parsbert-ner-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HooshvareLab/bert-base-parsbert-ner-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HooshvareLab/bert-base-parsbert-ner-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-ner-uncased") model = AutoModelForTokenClassification.from_pretrained("HooshvareLab/bert-base-parsbert-ner-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.txt from HooshvareLab/bert-base-parsbert-ner-uncased: direct link, hf CLI and curl.
- Browser
- Download file 142 Bytes
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https://huggingface.co/HooshvareLab/bert-base-parsbert-ner-uncased/resolve/main/eval_results.txt
- Command line
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hf download hf://HooshvareLab/bert-base-parsbert-ner-uncased/eval_results.txt
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curl -L -o eval_results.txt https://huggingface.co/HooshvareLab/bert-base-parsbert-ner-uncased/resolve/main/eval_results.txt
142 Bytes
| eval_loss = 0.027885852444273263 | |
| eval_precision = 0.9406858202038925 | |
| eval_recall = 0.959098272138229 | |
| eval_f1 = 0.9498028206670677 | |
| epoch = 3.0 | |