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 166
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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163.0,0.32662540982907085,0.7136365906026864,0.15070709586143494,14.5798,176.477,5.556,0.2499028371550719,107254
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164.0,0.3258130091476018,0.713399379565696,0.1506299078464508,14.6106,176.105,5.544,0.2514574426739215,107912
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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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| 164 |
163.0,0.32662540982907085,0.7136365906026864,0.15070709586143494,14.5798,176.477,5.556,0.2499028371550719,107254
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| 165 |
164.0,0.3258130091476018,0.713399379565696,0.1506299078464508,14.6106,176.105,5.544,0.2514574426739215,107912
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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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166.0,0.32534458818336726,0.7138433879344787,0.15059617161750793,14.3996,178.685,5.625,0.2495141857753595,109228
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