Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Tahsin/BERT-finetuned-conll2003-POS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tahsin/BERT-finetuned-conll2003-POS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Tahsin/BERT-finetuned-conll2003-POS")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Tahsin/BERT-finetuned-conll2003-POS") model = AutoModelForTokenClassification.from_pretrained("Tahsin/BERT-finetuned-conll2003-POS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Tahsin/BERT-finetuned-conll2003-POS: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/Tahsin/BERT-finetuned-conll2003-POS/resolve/main/training_args.bin
- Command line
-
hf download hf://Tahsin/BERT-finetuned-conll2003-POS/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Tahsin/BERT-finetuned-conll2003-POS/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- 9b789a04c185e58355c31fd2e05aa0a45e252dc29eaf1344e213a04fd7866496
- Size of remote file:
- 2.93 kB
- SHA256:
- 345134c44c3ffd9486fb3a7fba8844c5c9ccf941ff2a37e4cd28e6b0180a0bbe
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