Instructions to use apriadiazriel/bert_base_ncbi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apriadiazriel/bert_base_ncbi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="apriadiazriel/bert_base_ncbi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("apriadiazriel/bert_base_ncbi") model = AutoModelForTokenClassification.from_pretrained("apriadiazriel/bert_base_ncbi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 5dba910
Training in progress epoch 8
Browse files
README.md
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.0518
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- Train Precision: 0.8
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- Train Recall: 0.8640
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- Train F1: 0.8308
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- Train Accuracy: 0.9860
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- Epoch:
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## Model description
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| 0.0174 | 0.0518 | 0.8 | 0.8640 | 0.8308 | 0.9860 | 5 |
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| 0.0168 | 0.0518 | 0.8 | 0.8640 | 0.8308 | 0.9860 | 6 |
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| 0.0172 | 0.0518 | 0.8 | 0.8640 | 0.8308 | 0.9860 | 7 |
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### Framework versions
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.0167
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- Validation Loss: 0.0518
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- Train Precision: 0.8
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- Train Recall: 0.8640
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- Train F1: 0.8308
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- Train Accuracy: 0.9860
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- Epoch: 8
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## Model description
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| 0.0174 | 0.0518 | 0.8 | 0.8640 | 0.8308 | 0.9860 | 5 |
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| 0.0168 | 0.0518 | 0.8 | 0.8640 | 0.8308 | 0.9860 | 6 |
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| 0.0172 | 0.0518 | 0.8 | 0.8640 | 0.8308 | 0.9860 | 7 |
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| 0.0167 | 0.0518 | 0.8 | 0.8640 | 0.8308 | 0.9860 | 8 |
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### Framework versions
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