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") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 402
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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399.0,0.3471919542200232,0.722095219163105,0.14731723070144653,14.4382,178.207,5.61,0.25961912164788187,262542
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| 401 |
400.0,0.34592204408016863,0.722252569896211,0.14735309779644012,14.4014,178.663,5.624,0.25884181888845703,263200
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| 402 |
401.0,0.3471581690459195,0.7224783147459728,0.1473505198955536,14.4465,178.105,5.607,0.25806451612903225,263858
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| 400 |
399.0,0.3471919542200232,0.722095219163105,0.14731723070144653,14.4382,178.207,5.61,0.25961912164788187,262542
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| 401 |
400.0,0.34592204408016863,0.722252569896211,0.14735309779644012,14.4014,178.663,5.624,0.25884181888845703,263200
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| 402 |
401.0,0.3471581690459195,0.7224783147459728,0.1473505198955536,14.4465,178.105,5.607,0.25806451612903225,263858
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| 403 |
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402.0,0.3478531743994827,0.722310440651052,0.14732396602630615,14.6234,175.951,5.539,0.25728721336960747,264516
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403.0,0.3493686330072137,0.7223212517330164,0.14736512303352356,14.3618,179.156,5.64,0.25845316750874464,265174
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