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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 303
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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300.0,0.342344236697746,0.7204050230803594,0.14818550646305084,14.2949,179.994,5.666,0.25340069957248346,197400
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301.0,0.3419657792391,0.7197772141827042,0.14813284575939178,14.361,179.166,5.64,0.25573260785075785,198058
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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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300.0,0.342344236697746,0.7204050230803594,0.14818550646305084,14.2949,179.994,5.666,0.25340069957248346,197400
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301.0,0.3419657792391,0.7197772141827042,0.14813284575939178,14.361,179.166,5.64,0.25573260785075785,198058
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| 303 |
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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