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 121
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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118.0,0.3166799253041203,0.7094502841623498,0.15241198241710663,14.4732,177.777,5.597,0.2483482316362223,77644
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| 120 |
119.0,0.31819137359495175,0.7100977198697068,0.15246982872486115,14.5626,176.685,5.562,0.24485036921881073,78302
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| 121 |
120.0,0.31814870691154845,0.7096968782988992,0.15235722064971924,14.0849,182.678,5.751,0.24757092887679752,78960
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| 119 |
118.0,0.3166799253041203,0.7094502841623498,0.15241198241710663,14.4732,177.777,5.597,0.2483482316362223,77644
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| 120 |
119.0,0.31819137359495175,0.7100977198697068,0.15246982872486115,14.5626,176.685,5.562,0.24485036921881073,78302
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| 121 |
120.0,0.31814870691154845,0.7096968782988992,0.15235722064971924,14.0849,182.678,5.751,0.24757092887679752,78960
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| 122 |
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121.0,0.317213298696518,0.709754997745378,0.1524665504693985,14.5386,176.977,5.571,0.24368441507967353,79618
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