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 109
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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106.0,0.3145457990502851,0.7079023998393413,0.15321803092956543,14.2991,179.942,5.665,0.24368441507967353,69748
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| 108 |
107.0,0.3134945314852967,0.7077696756349007,0.15315872430801392,14.5544,176.786,5.565,0.24523902059852312,70406
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| 109 |
108.0,0.3129180450428549,0.707880981101729,0.15306010842323303,14.5016,177.428,5.586,0.24485036921881073,71064
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| 107 |
106.0,0.3145457990502851,0.7079023998393413,0.15321803092956543,14.2991,179.942,5.665,0.24368441507967353,69748
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| 108 |
107.0,0.3134945314852967,0.7077696756349007,0.15315872430801392,14.5544,176.786,5.565,0.24523902059852312,70406
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| 109 |
108.0,0.3129180450428549,0.707880981101729,0.15306010842323303,14.5016,177.428,5.586,0.24485036921881073,71064
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| 110 |
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109.0,0.3147812184455094,0.7086282186417708,0.1530476063489914,14.4531,178.024,5.604,0.24368441507967353,71722
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