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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 556
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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553.0,0.35047228711253214,0.724180673310526,0.14667242765426636,14.4713,177.8,5.597,0.26117372716673143,363874
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554.0,0.350216974226131,0.7239598848177937,0.14660093188285828,14.6386,175.768,5.533,0.26078507578701904,364532
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| 556 |
555.0,0.3498819045670741,0.7236554871998412,0.14662127196788788,14.3887,178.821,5.629,0.25961912164788187,365190
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| 554 |
553.0,0.35047228711253214,0.724180673310526,0.14667242765426636,14.4713,177.8,5.597,0.26117372716673143,363874
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| 555 |
554.0,0.350216974226131,0.7239598848177937,0.14660093188285828,14.6386,175.768,5.533,0.26078507578701904,364532
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| 556 |
555.0,0.3498819045670741,0.7236554871998412,0.14662127196788788,14.3887,178.821,5.629,0.25961912164788187,365190
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556.0,0.3503243398263119,0.723640509592029,0.14664532244205475,14.4224,178.403,5.616,0.2592304702681695,365848
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