Instructions to use isspek/roberta-base_monkeypox_top3_2_2e-5_16_undersampling_0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use isspek/roberta-base_monkeypox_top3_2_2e-5_16_undersampling_0.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="isspek/roberta-base_monkeypox_top3_2_2e-5_16_undersampling_0.3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("isspek/roberta-base_monkeypox_top3_2_2e-5_16_undersampling_0.3") model = AutoModelForSequenceClassification.from_pretrained("isspek/roberta-base_monkeypox_top3_2_2e-5_16_undersampling_0.3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload RobertaForSequenceClassification
Browse files- config.json +2 -2
- model.safetensors +1 -1
config.json
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{
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"_name_or_path": "
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"architectures": [
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"RobertaForSequenceClassification"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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{
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"_name_or_path": "models/roberta-base_monkeypox_top3_2_2e-5_16_undersampling_0.3",
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"architectures": [
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"RobertaForSequenceClassification"
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],
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.41.0",
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"type_vocab_size": 1,
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"use_cache": true,
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"vocab_size": 50265
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model.safetensors
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size 498612824
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version https://git-lfs.github.com/spec/v1
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size 498612824
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