Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 210
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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207.0,0.3317484621176259,0.7165652737608457,0.14954958856105804,14.2643,180.38,5.679,0.2518460940536339,136206
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209.0,0.33330233028042766,0.7167272908465793,0.14955168962478638,14.5104,177.322,5.582,0.2526233968130587,137522
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207.0,0.3317484621176259,0.7165652737608457,0.14954958856105804,14.2643,180.38,5.679,0.2518460940536339,136206
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| 210 |
209.0,0.33330233028042766,0.7167272908465793,0.14955168962478638,14.5104,177.322,5.582,0.2526233968130587,137522
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210.0,0.3346079410427036,0.7171525559105432,0.14940455555915833,14.5055,177.382,5.584,0.2541780023319083,138180
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