Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
emotion-classification
midwest-emo
math-rock
domain-adaptation
hybrid-corpus
Eval Results (legacy)
text-embeddings-inference
Instructions to use anggars/xlm-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anggars/xlm-emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anggars/xlm-emotion")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anggars/xlm-emotion") model = AutoModelForSequenceClassification.from_pretrained("anggars/xlm-emotion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer.json +2 -2
- tokenizer_config.json +1 -0
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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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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"is_local": false,
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"mask_token": "<mask>",
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"model_max_length": 512,
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"pad_token": "<pad>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"is_local": false,
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"local_files_only": false,
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"mask_token": "<mask>",
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"model_max_length": 512,
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"pad_token": "<pad>",
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