Instructions to use tner/xlm-roberta-base-bc5cdr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/xlm-roberta-base-bc5cdr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/xlm-roberta-base-bc5cdr")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/xlm-roberta-base-bc5cdr") model = AutoModelForTokenClassification.from_pretrained("tner/xlm-roberta-base-bc5cdr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download parameter.json from tner/xlm-roberta-base-bc5cdr: direct link, hf CLI and curl.
- Browser
- Download file 262 Bytes
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https://huggingface.co/tner/xlm-roberta-base-bc5cdr/resolve/main/parameter.json
- Command line
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hf download hf://tner/xlm-roberta-base-bc5cdr/parameter.json
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curl -L -o parameter.json https://huggingface.co/tner/xlm-roberta-base-bc5cdr/resolve/main/parameter.json
262 Bytes
| {"dataset": ["bc5cdr"], "transformers_model": "xlm-roberta-base", "random_seed": 1234, "lr": 1e-05, "total_step": 13000, "warmup_step": 700, "weight_decay": 1e-07, "batch_size": 16, "max_seq_length": 128, "fp16": false, "max_grad_norm": 1.0, "lower_case": false} |