Text Classification
Transformers
Safetensors
bert
proteins
Biology
classifier
text-embeddings-inference
Instructions to use oohtmeel/Bert_protein_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oohtmeel/Bert_protein_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oohtmeel/Bert_protein_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oohtmeel/Bert_protein_classifier") model = AutoModelForSequenceClassification.from_pretrained("oohtmeel/Bert_protein_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 400
Browse files- config.json +24 -86
- model.safetensors +2 -2
- training_args.bin +1 -1
config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size
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version https://git-lfs.github.com/spec/v1
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size 437989408
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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size 5048
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version https://git-lfs.github.com/spec/v1
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size 5048
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