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 583
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 581 |
580.0,0.3498016783996152,0.7237188073622067,0.14655892550945282,14.4433,178.145,5.608,0.26000777302759426,381640
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| 582 |
581.0,0.35003747958417086,0.7238180284764598,0.1465616524219513,14.5994,176.241,5.548,0.26000777302759426,382298
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| 583 |
582.0,0.35031943220443923,0.7238095238095238,0.1465374082326889,14.3623,179.15,5.64,0.26039642440730665,382956
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| 581 |
580.0,0.3498016783996152,0.7237188073622067,0.14655892550945282,14.4433,178.145,5.608,0.26000777302759426,381640
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| 582 |
581.0,0.35003747958417086,0.7238180284764598,0.1465616524219513,14.5994,176.241,5.548,0.26000777302759426,382298
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| 583 |
582.0,0.35031943220443923,0.7238095238095238,0.1465374082326889,14.3623,179.15,5.64,0.26039642440730665,382956
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| 584 |
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583.0,0.35019996018785865,0.7238180284764598,0.1465137004852295,14.4308,178.299,5.613,0.26000777302759426,383614
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