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 338
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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335.0,0.3428948949132397,0.7211060274517604,0.1477837860584259,14.6393,175.76,5.533,0.2592304702681695,220430
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| 337 |
336.0,0.343567341997816,0.7209741550695825,0.14774557948112488,14.427,178.346,5.614,0.25767586474931986,221088
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| 338 |
337.0,0.34309821452648376,0.7204573701217997,0.14778682589530945,14.5854,176.409,5.553,0.2565099106101827,221746
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| 336 |
335.0,0.3428948949132397,0.7211060274517604,0.1477837860584259,14.6393,175.76,5.533,0.2592304702681695,220430
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| 337 |
336.0,0.343567341997816,0.7209741550695825,0.14774557948112488,14.427,178.346,5.614,0.25767586474931986,221088
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| 338 |
337.0,0.34309821452648376,0.7204573701217997,0.14778682589530945,14.5854,176.409,5.553,0.2565099106101827,221746
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| 339 |
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338.0,0.3428453776566097,0.7206437512418041,0.14777711033821106,14.638,175.776,5.534,0.2568985619898951,222404
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