Text Classification
Transformers
ONNX
Safetensors
multilingual
modernbert
mmbert-32k
yarn-rope
merged-model
text-embeddings-inference
Instructions to use llm-semantic-router/mmbert32k-factcheck-classifier-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-semantic-router/mmbert32k-factcheck-classifier-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="llm-semantic-router/mmbert32k-factcheck-classifier-merged")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/mmbert32k-factcheck-classifier-merged") model = AutoModelForSequenceClassification.from_pretrained("llm-semantic-router/mmbert32k-factcheck-classifier-merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config.json with huggingface_hub
Browse files- config.json +11 -2
config.json
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"sparse_pred_ignore_index": -100,
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"sparse_prediction": false,
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"transformers_version": "4.57.6",
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"vocab_size": 256000
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"sparse_pred_ignore_index": -100,
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"sparse_prediction": false,
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"transformers_version": "4.57.6",
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"vocab_size": 256000,
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"id2label": {
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"0": "NO_FACT_CHECK_NEEDED",
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"1": "FACT_CHECK_NEEDED"
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},
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"label2id": {
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"NO_FACT_CHECK_NEEDED": 0,
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"FACT_CHECK_NEEDED": 1
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},
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"num_labels": 2
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}
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