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 332
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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329.0,0.34281610988818384,0.7205685037022312,0.1478181928396225,14.4451,178.122,5.607,0.25728721336960747,216482
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330.0,0.3428785233432568,0.7208677480346303,0.14780260622501373,14.8131,173.698,5.468,0.25884181888845703,217140
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| 332 |
331.0,0.34151716575909524,0.7204295729130413,0.14781752228736877,14.4016,178.661,5.624,0.25728721336960747,217798
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| 330 |
329.0,0.34281610988818384,0.7205685037022312,0.1478181928396225,14.4451,178.122,5.607,0.25728721336960747,216482
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| 331 |
330.0,0.3428785233432568,0.7208677480346303,0.14780260622501373,14.8131,173.698,5.468,0.25884181888845703,217140
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| 332 |
331.0,0.34151716575909524,0.7204295729130413,0.14781752228736877,14.4016,178.661,5.624,0.25728721336960747,217798
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| 333 |
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332.0,0.34282369419247527,0.7207314649175114,0.14780935645103455,14.5932,176.315,5.551,0.25767586474931986,218456
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