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 566
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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563.0,0.3506558173081528,0.7241003271537623,0.14659501612186432,14.3923,178.776,5.628,0.26078507578701904,370454
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| 565 |
564.0,0.3498257469732833,0.7236724565756824,0.14658716320991516,14.417,178.47,5.618,0.26039642440730665,371112
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| 566 |
565.0,0.35070335391983165,0.7243087900109008,0.14662356674671173,14.2155,180.999,5.698,0.26078507578701904,371770
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| 564 |
563.0,0.3506558173081528,0.7241003271537623,0.14659501612186432,14.3923,178.776,5.628,0.26078507578701904,370454
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| 565 |
564.0,0.3498257469732833,0.7236724565756824,0.14658716320991516,14.417,178.47,5.618,0.26039642440730665,371112
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| 566 |
565.0,0.35070335391983165,0.7243087900109008,0.14662356674671173,14.2155,180.999,5.698,0.26078507578701904,371770
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| 567 |
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566.0,0.350350841848224,0.7238435576732182,0.1465800255537033,14.7349,174.62,5.497,0.26039642440730665,372428
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