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 515
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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512.0,0.3498522796866859,0.7236235690569404,0.14677901566028595,14.5633,176.677,5.562,0.25961912164788187,336896
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513.0,0.348508500424691,0.7233472304943419,0.14671696722507477,14.4527,178.029,5.604,0.25961912164788187,337554
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| 515 |
514.0,0.34997204784552877,0.7240559024680345,0.14681805670261383,14.6349,175.813,5.535,0.26117372716673143,338212
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| 513 |
512.0,0.3498522796866859,0.7236235690569404,0.14677901566028595,14.5633,176.677,5.562,0.25961912164788187,336896
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| 514 |
513.0,0.348508500424691,0.7233472304943419,0.14671696722507477,14.4527,178.029,5.604,0.25961912164788187,337554
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| 515 |
514.0,0.34997204784552877,0.7240559024680345,0.14681805670261383,14.6349,175.813,5.535,0.26117372716673143,338212
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515.0,0.3495831549123265,0.7235966219572777,0.14676524698734283,14.4953,177.505,5.588,0.26117372716673143,338870
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