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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 77
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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74.0,0.2932262485923011,0.701192400970089,0.1559084802865982,14.4747,177.759,5.596,0.23280217644772638,48692
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| 76 |
75.0,0.29596558310827953,0.7018022111161594,0.15575554966926575,14.3396,179.433,5.649,0.2308589195491644,49350
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| 77 |
76.0,0.2957956067490935,0.7021801520567947,0.15572988986968994,14.4793,177.702,5.594,0.23163622230858918,50008
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| 75 |
74.0,0.2932262485923011,0.701192400970089,0.1559084802865982,14.4747,177.759,5.596,0.23280217644772638,48692
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| 76 |
75.0,0.29596558310827953,0.7018022111161594,0.15575554966926575,14.3396,179.433,5.649,0.2308589195491644,49350
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| 77 |
76.0,0.2957956067490935,0.7021801520567947,0.15572988986968994,14.4793,177.702,5.594,0.23163622230858918,50008
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| 78 |
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77.0,0.2942873187016953,0.7011883691529709,0.15558689832687378,14.6081,176.135,5.545,0.23396813058686358,50666
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