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 367
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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364.0,0.3444814450883252,0.7213651470807083,0.14756028354167938,14.5408,176.95,5.571,0.2568985619898951,239512
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| 366 |
365.0,0.3456984842863253,0.7211371204214502,0.1474786102771759,14.4946,177.515,5.588,0.25767586474931986,240170
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| 367 |
366.0,0.34578193038222643,0.7212756445283394,0.14752605557441711,14.3193,179.687,5.657,0.25767586474931986,240828
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| 365 |
364.0,0.3444814450883252,0.7213651470807083,0.14756028354167938,14.5408,176.95,5.571,0.2568985619898951,239512
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| 366 |
365.0,0.3456984842863253,0.7211371204214502,0.1474786102771759,14.4946,177.515,5.588,0.25767586474931986,240170
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| 367 |
366.0,0.34578193038222643,0.7212756445283394,0.14752605557441711,14.3193,179.687,5.657,0.25767586474931986,240828
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| 368 |
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367.0,0.34534327904343004,0.721469347232564,0.1475464105606079,14.5507,176.829,5.567,0.25728721336960747,241486
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