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 279
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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276.0,0.3381920748020486,0.7189210709664576,0.1484980583190918,14.4167,178.474,5.618,0.2541780023319083,181608
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277.0,0.33838876069396673,0.7188775002487809,0.1484721153974533,14.4714,177.799,5.597,0.2541780023319083,182266
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| 279 |
278.0,0.33930429585168853,0.7192545345824197,0.14839613437652588,14.4767,177.734,5.595,0.2549553050913331,182924
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276.0,0.3381920748020486,0.7189210709664576,0.1484980583190918,14.4167,178.474,5.618,0.2541780023319083,181608
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| 278 |
277.0,0.33838876069396673,0.7188775002487809,0.1484721153974533,14.4714,177.799,5.597,0.2541780023319083,182266
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| 279 |
278.0,0.33930429585168853,0.7192545345824197,0.14839613437652588,14.4767,177.734,5.595,0.2549553050913331,182924
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279.0,0.33967414374734906,0.719084349340632,0.14842939376831055,14.402,178.655,5.624,0.2541780023319083,183582
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