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")# 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 532
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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529.0,0.35009037070246374,0.7235267834979894,0.14671511948108673,14.3561,179.226,5.642,0.25961912164788187,348082
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530.0,0.35001801154907647,0.723746217570316,0.14670266211032867,14.414,178.507,5.62,0.2592304702681695,348740
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531.0,0.34942979982409395,0.7235966219572777,0.1467140167951584,14.4351,178.246,5.611,0.26078507578701904,349398
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| 530 |
529.0,0.35009037070246374,0.7235267834979894,0.14671511948108673,14.3561,179.226,5.642,0.25961912164788187,348082
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| 531 |
530.0,0.35001801154907647,0.723746217570316,0.14670266211032867,14.414,178.507,5.62,0.2592304702681695,348740
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| 532 |
531.0,0.34942979982409395,0.7235966219572777,0.1467140167951584,14.4351,178.246,5.611,0.26078507578701904,349398
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532.0,0.35025465960312185,0.7236933452345532,0.14677557349205017,14.2873,180.09,5.669,0.26039642440730665,350056
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