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 466
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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463.0,0.34866857673208285,0.7229776674937965,0.14697597920894623,14.3923,178.777,5.628,0.25961912164788187,304654
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| 465 |
464.0,0.3489951460880334,0.7231784137691583,0.14700351655483246,14.5148,177.268,5.581,0.2592304702681695,305312
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| 466 |
465.0,0.34809810775297345,0.7226440375167485,0.14698365330696106,14.3372,179.463,5.65,0.25845316750874464,305970
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| 464 |
463.0,0.34866857673208285,0.7229776674937965,0.14697597920894623,14.3923,178.777,5.628,0.25961912164788187,304654
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| 465 |
464.0,0.3489951460880334,0.7231784137691583,0.14700351655483246,14.5148,177.268,5.581,0.2592304702681695,305312
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| 466 |
465.0,0.34809810775297345,0.7226440375167485,0.14698365330696106,14.3372,179.463,5.65,0.25845316750874464,305970
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| 467 |
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466.0,0.3489150795886313,0.7233915806195393,0.14695455133914948,14.5507,176.83,5.567,0.25884181888845703,306628
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| 468 |
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467.0,0.3502601359139448,0.7232545463554829,0.14702273905277252,14.3723,179.025,5.636,0.26000777302759426,307286
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