Text Classification
Transformers
Safetensors
Catalan
roberta
eamt-2026
catalan
valencian
translationese
reward-model
text-embeddings-inference
Instructions to use guerreropaula/ht_mt_classifier_best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guerreropaula/ht_mt_classifier_best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="guerreropaula/ht_mt_classifier_best")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("guerreropaula/ht_mt_classifier_best") model = AutoModelForSequenceClassification.from_pretrained("guerreropaula/ht_mt_classifier_best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer.json +2 -16
- tokenizer_config.json +1 -1
tokenizer.json
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"version": "1.0",
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"truncation":
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"max_length": 128,
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"strategy": "LongestFirst",
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"stride": 0
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"padding": {
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"strategy": {
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"Fixed": 128
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"direction": "Right",
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"pad_to_multiple_of": null,
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"pad_id": 1,
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"pad_type_id": 0,
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"pad_token": "<pad>"
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"added_tokens": [
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"id": 0,
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"version": "1.0",
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"truncation": null,
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"padding": null,
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tokenizer_config.json
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"cls_token": "<s>",
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"eos_token": "</s>",
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"errors": "replace",
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"is_local":
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"mask_token": "<mask>",
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"max_len": 512,
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"model_max_length": 512,
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"cls_token": "<s>",
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"eos_token": "</s>",
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"errors": "replace",
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"is_local": true,
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"mask_token": "<mask>",
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"max_len": 512,
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"model_max_length": 512,
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