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 578
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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575.0,0.3499144185338867,0.7239446401111166,0.14660924673080444,14.3328,179.519,5.651,0.26078507578701904,378350
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576.0,0.3498104765510311,0.7237972295318008,0.14654354751110077,14.369,179.066,5.637,0.26000777302759426,379008
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| 578 |
577.0,0.35003164166062223,0.7238916988991372,0.1466130167245865,14.3435,179.385,5.647,0.26078507578701904,379666
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| 576 |
575.0,0.3499144185338867,0.7239446401111166,0.14660924673080444,14.3328,179.519,5.651,0.26078507578701904,378350
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| 577 |
576.0,0.3498104765510311,0.7237972295318008,0.14654354751110077,14.369,179.066,5.637,0.26000777302759426,379008
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| 578 |
577.0,0.35003164166062223,0.7238916988991372,0.1466130167245865,14.3435,179.385,5.647,0.26078507578701904,379666
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578.0,0.350186387320148,0.7238576667657846,0.14656561613082886,14.6309,175.86,5.536,0.2592304702681695,380324
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