Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use intermezzo672/NHS-dmis-binary-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intermezzo672/NHS-dmis-binary-512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="intermezzo672/NHS-dmis-binary-512")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("intermezzo672/NHS-dmis-binary-512") model = AutoModelForSequenceClassification.from_pretrained("intermezzo672/NHS-dmis-binary-512") - Notebooks
- Google Colab
- Kaggle
NHS-dmis-binary-512
Browse files
README.md
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This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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## Model description
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This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4235
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- Accuracy: 0.8125
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- Precision: 0.8080
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- Recall: 0.8104
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- F1: 0.8090
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## Model description
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runs/May02_13-51-03_bd3f76a67452/events.out.tfevents.1714659922.bd3f76a67452.330.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:70d8bce55b80a76312f4781e5791a94194a052b74428e3f4e92f4778ec83becc
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size 560
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