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 111
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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108.0,0.3129180450428549,0.707880981101729,0.15306010842323303,14.5016,177.428,5.586,0.24485036921881073,71064
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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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| 111 |
110.0,0.31486023334150576,0.7088582183186951,0.15295182168483734,14.4554,177.996,5.603,0.24485036921881073,72380
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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 |
109.0,0.3147812184455094,0.7086282186417708,0.1530476063489914,14.4531,178.024,5.604,0.24368441507967353,71722
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| 111 |
110.0,0.31486023334150576,0.7088582183186951,0.15295182168483734,14.4554,177.996,5.603,0.24485036921881073,72380
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111.0,0.3156943232261125,0.7088950137810073,0.15296627581119537,14.4864,177.615,5.591,0.24407306645938592,73038
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