Instructions to use Sayan01/DBERT2_sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sayan01/DBERT2_sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sayan01/DBERT2_sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sayan01/DBERT2_sst2") model = AutoModelForSequenceClassification.from_pretrained("Sayan01/DBERT2_sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 1
Browse files
config.json
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"output_hidden_states": true,
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"problem_type": "single_label_classification",
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_layers": 8,
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"output_hidden_states": true,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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pytorch_model.bin
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