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 424
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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421.0,0.34661189850202345,0.7224043715846995,0.14718474447727203,14.6395,175.758,5.533,0.2592304702681695,277018
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422.0,0.3473354549207439,0.7223492000397496,0.14717230200767517,14.4271,178.345,5.614,0.26039642440730665,277676
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| 424 |
423.0,0.34734503451786763,0.7220374323586357,0.1472797989845276,14.2646,180.377,5.678,0.25728721336960747,278334
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421.0,0.34661189850202345,0.7224043715846995,0.14718474447727203,14.6395,175.758,5.533,0.2592304702681695,277018
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| 423 |
422.0,0.3473354549207439,0.7223492000397496,0.14717230200767517,14.4271,178.345,5.614,0.26039642440730665,277676
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| 424 |
423.0,0.34734503451786763,0.7220374323586357,0.1472797989845276,14.2646,180.377,5.678,0.25728721336960747,278334
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| 425 |
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424.0,0.34698638681168303,0.7220758562409902,0.14719258248806,14.6499,175.633,5.529,0.2592304702681695,278992
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425.0,0.3471518320235766,0.7222001193554805,0.1470671445131302,14.2644,180.379,5.678,0.25961912164788187,279650
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