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 305
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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302.0,0.34109198554655623,0.7200557796703023,0.14807672798633575,14.3026,179.898,5.663,0.25573260785075785,198716
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303.0,0.34197948663707206,0.7199444058373871,0.14819973707199097,14.5357,177.012,5.572,0.2549553050913331,199374
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| 305 |
304.0,0.34156065068622454,0.720183029941311,0.1481110155582428,14.3269,179.592,5.654,0.2565099106101827,200032
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302.0,0.34109198554655623,0.7200557796703023,0.14807672798633575,14.3026,179.898,5.663,0.25573260785075785,198716
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| 304 |
303.0,0.34197948663707206,0.7199444058373871,0.14819973707199097,14.5357,177.012,5.572,0.2549553050913331,199374
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| 305 |
304.0,0.34156065068622454,0.720183029941311,0.1481110155582428,14.3269,179.592,5.654,0.2565099106101827,200032
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305.0,0.3416593225285195,0.7202428343949044,0.14809446036815643,14.5225,177.173,5.578,0.25612125923047024,200690
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