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 329
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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326.0,0.341807310123352,0.7204900886542485,0.14783377945423126,14.5363,177.005,5.572,0.2565099106101827,214508
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| 328 |
327.0,0.34345725452344894,0.7209221443831669,0.14785908162593842,14.3682,179.076,5.637,0.25806451612903225,215166
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| 329 |
328.0,0.3436511799805986,0.7206320810971973,0.14785687625408173,14.4133,178.516,5.62,0.25612125923047024,215824
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| 327 |
326.0,0.341807310123352,0.7204900886542485,0.14783377945423126,14.5363,177.005,5.572,0.2565099106101827,214508
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| 328 |
327.0,0.34345725452344894,0.7209221443831669,0.14785908162593842,14.3682,179.076,5.637,0.25806451612903225,215166
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| 329 |
328.0,0.3436511799805986,0.7206320810971973,0.14785687625408173,14.4133,178.516,5.62,0.25612125923047024,215824
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| 330 |
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329.0,0.34281610988818384,0.7205685037022312,0.1478181928396225,14.4451,178.122,5.607,0.25728721336960747,216482
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