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 365
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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362.0,0.34399816310758197,0.7212039934436001,0.14762039482593536,14.598,176.257,5.549,0.2565099106101827,238196
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| 364 |
363.0,0.34407463364311414,0.7215233928305076,0.147491917014122,14.2307,180.806,5.692,0.2592304702681695,238854
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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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| 363 |
362.0,0.34399816310758197,0.7212039934436001,0.14762039482593536,14.598,176.257,5.549,0.2565099106101827,238196
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| 364 |
363.0,0.34407463364311414,0.7215233928305076,0.147491917014122,14.2307,180.806,5.692,0.2592304702681695,238854
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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 |
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365.0,0.3456984842863253,0.7211371204214502,0.1474786102771759,14.4946,177.515,5.588,0.25767586474931986,240170
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