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 509
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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506.0,0.3490918998203231,0.72347601805466,0.1468096673488617,14.6921,175.128,5.513,0.2592304702681695,332948
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| 508 |
507.0,0.3495568712693217,0.7236515347173935,0.14673784375190735,14.4272,178.343,5.614,0.26078507578701904,333606
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| 509 |
508.0,0.3480373364535791,0.7230340988169798,0.14675956964492798,14.3504,179.298,5.644,0.26000777302759426,334264
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| 507 |
506.0,0.3490918998203231,0.72347601805466,0.1468096673488617,14.6921,175.128,5.513,0.2592304702681695,332948
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| 508 |
507.0,0.3495568712693217,0.7236515347173935,0.14673784375190735,14.4272,178.343,5.614,0.26078507578701904,333606
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| 509 |
508.0,0.3480373364535791,0.7230340988169798,0.14675956964492798,14.3504,179.298,5.644,0.26000777302759426,334264
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| 510 |
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509.0,0.3498300863272387,0.7237972295318008,0.14675353467464447,14.5388,176.975,5.571,0.26039642440730665,334922
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