Feature Extraction
Transformers
Safetensors
English
bert
token-classification
text-embeddings-inference
Instructions to use noystl/scibert_token_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noystl/scibert_token_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noystl/scibert_token_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("noystl/scibert_token_classifier") model = AutoModelForTokenClassification.from_pretrained("noystl/scibert_token_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This is a [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model, fine-tuned on the [Recombination-extraction](https://huggingface.co/datasets/noystl/Recombination-Extraction) dataset.
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**Non-Default Hyperparameters**
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* `max_steps`: 500
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* `weight_decay`: 0.1
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* `learning_rate`: 6.e-5
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**Bibtex**
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```bibtex
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This is a [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) model, fine-tuned on the [Recombination-extraction](https://huggingface.co/datasets/noystl/Recombination-Extraction) dataset.
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**Non-Default Hyperparameters**
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* `per_device_train_batch_size`: 1
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* `max_steps`: 500
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* `weight_decay`: 0.1
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* `learning_rate`: 6.e-5
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* `warmup_ratio`: 0.1
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**Bibtex**
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```bibtex
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