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 588
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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585.0,0.35055500208478163,0.723902052146327,0.14656244218349457,14.4364,178.23,5.611,0.25961912164788187,384930
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586.0,0.3501453451920695,0.723807633133158,0.14653097093105316,14.3401,179.426,5.648,0.26039642440730665,385588
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| 588 |
587.0,0.3504797217145777,0.7240712266256634,0.14651574194431305,14.3367,179.47,5.65,0.26039642440730665,386246
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| 586 |
585.0,0.35055500208478163,0.723902052146327,0.14656244218349457,14.4364,178.23,5.611,0.25961912164788187,384930
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| 587 |
586.0,0.3501453451920695,0.723807633133158,0.14653097093105316,14.3401,179.426,5.648,0.26039642440730665,385588
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| 588 |
587.0,0.3504797217145777,0.7240712266256634,0.14651574194431305,14.3367,179.47,5.65,0.26039642440730665,386246
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| 589 |
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588.0,0.3495134232533033,0.723699880905121,0.14652381837368011,14.2648,180.374,5.678,0.25961912164788187,386904
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