Token Classification
Transformers
PyTorch
Safetensors
English
esm
biology
esm2
ESM-2
protein language model
Instructions to use AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2") model = AutoModelForTokenClassification.from_pretrained("AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Parent(s): b73ac0e
Update README.md
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README.md
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@@ -45,7 +45,7 @@ This model was trained on approximately 70,000 proteins with binding site and ac
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The training split was a random 85/15 split for this version, and does not consider anything in the way of family or sequence
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similarity. New iterations of the model have been trained on larger datasets (over 200,000 proteins), with the split such that
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there are no overlapping families, however they seem to overfit much earlier and have significantly worse performance in terms
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of the training metrics (precision, recall, and F1).
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Training Metrics for the Model in the form of the `trainer_state.json` can be
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[found here](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2/blob/main/trainer_state.json).
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The training split was a random 85/15 split for this version, and does not consider anything in the way of family or sequence
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| 46 |
similarity. New iterations of the model have been trained on larger datasets (over 200,000 proteins), with the split such that
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| 47 |
there are no overlapping families, however they seem to overfit much earlier and have significantly worse performance in terms
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of the training metrics (precision, recall, and F1). To address this we plan to implement LoRA (and hopefully QLoRA).
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Training Metrics for the Model in the form of the `trainer_state.json` can be
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[found here](https://huggingface.co/AmelieSchreiber/esm2_t6_8M_general_binding_sites_v2/blob/main/trainer_state.json).
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