Instructions to use hf-internal-testing/tiny-random-EuroBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EuroBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-EuroBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-EuroBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-EuroBertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
Upload EuroBertForSequenceClassification
Browse files- config.json +1 -1
- model.safetensors +2 -2
config.json
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"model_type": "eurobert",
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"num_attention_heads": 2,
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"num_hidden_layers": 4,
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"num_key_value_heads":
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"pad_token": "<|end_of_text|>",
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"pad_token_id": 128001,
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"pretraining_tp": 1,
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"model_type": "eurobert",
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"num_attention_heads": 2,
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"num_hidden_layers": 4,
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"num_key_value_heads": 2,
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"pad_token": "<|end_of_text|>",
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"pad_token_id": 128001,
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"pretraining_tp": 1,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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size 16787224
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