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 246
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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243.0,0.3370310518497063,0.7181917972258257,0.14889003336429596,14.4497,178.066,5.606,0.25534395647104546,159894
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244.0,0.33811148966254845,0.71832387777446,0.1488657146692276,14.4367,178.226,5.611,0.25301204819277107,160552
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| 246 |
245.0,0.33636802003257293,0.7175313327008539,0.14884239435195923,14.6555,175.565,5.527,0.25340069957248346,161210
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| 244 |
243.0,0.3370310518497063,0.7181917972258257,0.14889003336429596,14.4497,178.066,5.606,0.25534395647104546,159894
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| 245 |
244.0,0.33811148966254845,0.71832387777446,0.1488657146692276,14.4367,178.226,5.611,0.25301204819277107,160552
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| 246 |
245.0,0.33636802003257293,0.7175313327008539,0.14884239435195923,14.6555,175.565,5.527,0.25340069957248346,161210
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| 247 |
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246.0,0.33742838394694497,0.718333664744482,0.14889340102672577,14.4552,177.999,5.604,0.2522347454333463,161868
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