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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahnaf702/SentibertLarge") model = AutoModelForSequenceClassification.from_pretrained("ahnaf702/SentibertLarge") - Notebooks
- Google Colab
- Kaggle
Upload ElectraForSequenceClassification
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"ElectraForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": 0.
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"embedding_size": 128,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"ElectraForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": 0.0,
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"embedding_size": 128,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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pytorch_model.bin
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size 55023609
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