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 168
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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165.0,0.3264549367083365,0.7139005392450569,0.1507815271615982,14.4402,178.184,5.609,0.2483482316362223,108570
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| 167 |
166.0,0.32534458818336726,0.7138433879344787,0.15059617161750793,14.3996,178.685,5.625,0.2495141857753595,109228
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| 168 |
167.0,0.3287529758341488,0.7143924674936482,0.1506654918193817,14.5019,177.425,5.585,0.24873688301593472,109886
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| 166 |
165.0,0.3264549367083365,0.7139005392450569,0.1507815271615982,14.4402,178.184,5.609,0.2483482316362223,108570
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| 167 |
166.0,0.32534458818336726,0.7138433879344787,0.15059617161750793,14.3996,178.685,5.625,0.2495141857753595,109228
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| 168 |
167.0,0.3287529758341488,0.7143924674936482,0.1506654918193817,14.5019,177.425,5.585,0.24873688301593472,109886
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168.0,0.32935049167218033,0.7143141747185975,0.15063323080539703,14.5708,176.586,5.559,0.2483482316362223,110544
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