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 504
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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501.0,0.3485693512672971,0.7230746313123789,0.14681969583034515,14.5562,176.763,5.565,0.26000777302759426,329658
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| 503 |
502.0,0.3505818020415032,0.7242627013630731,0.14679555594921112,14.5423,176.932,5.57,0.26195102992615626,330316
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| 504 |
503.0,0.3494669565948187,0.7234464959301171,0.14679665863513947,14.6077,176.14,5.545,0.26078507578701904,330974
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| 502 |
501.0,0.3485693512672971,0.7230746313123789,0.14681969583034515,14.5562,176.763,5.565,0.26000777302759426,329658
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| 503 |
502.0,0.3505818020415032,0.7242627013630731,0.14679555594921112,14.5423,176.932,5.57,0.26195102992615626,330316
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| 504 |
503.0,0.3494669565948187,0.7234464959301171,0.14679665863513947,14.6077,176.14,5.545,0.26078507578701904,330974
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| 505 |
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504.0,0.3486733439112054,0.7231357742560485,0.1467479020357132,14.5706,176.588,5.559,0.26000777302759426,331632
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