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 576
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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573.0,0.350134077984241,0.7239738251041047,0.14662286639213562,14.6399,175.752,5.533,0.26000777302759426,377034
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574.0,0.3500400535717508,0.7238302934179223,0.14662466943264008,14.3956,178.735,5.627,0.2592304702681695,377692
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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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| 574 |
573.0,0.350134077984241,0.7239738251041047,0.14662286639213562,14.6399,175.752,5.533,0.26000777302759426,377034
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| 575 |
574.0,0.3500400535717508,0.7238302934179223,0.14662466943264008,14.3956,178.735,5.627,0.2592304702681695,377692
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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 |
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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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