Instructions to use ahnaf702/SentibertLarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahnaf702/SentibertLarge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ahnaf702/SentibertLarge")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahnaf702/SentibertLarge") model = AutoModelForSequenceClassification.from_pretrained("ahnaf702/SentibertLarge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload ElectraForSequenceClassification
Browse files- config.json +1 -0
- pytorch_model.bin +2 -2
config.json
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"summary_activation": "gelu",
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"summary_last_dropout": 0.1,
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"summary_type": "first",
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"problem_type": "single_label_classification",
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"summary_activation": "gelu",
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"summary_last_dropout": 0.1,
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"summary_type": "first",
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 55023609
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