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 136
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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133.0,0.31979688572920695,0.7108795832498497,0.15191353857517242,14.3018,179.907,5.664,0.24485036921881073,87514
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134.0,0.3199053478408629,0.7108819701671839,0.15184073150157928,14.3959,178.732,5.627,0.24329576369996114,88172
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| 136 |
135.0,0.319948159429496,0.7101979453770985,0.1517277956008911,14.4264,178.354,5.615,0.24640497473766032,88830
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| 134 |
133.0,0.31979688572920695,0.7108795832498497,0.15191353857517242,14.3018,179.907,5.664,0.24485036921881073,87514
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| 135 |
134.0,0.3199053478408629,0.7108819701671839,0.15184073150157928,14.3959,178.732,5.627,0.24329576369996114,88172
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| 136 |
135.0,0.319948159429496,0.7101979453770985,0.1517277956008911,14.4264,178.354,5.615,0.24640497473766032,88830
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136.0,0.3211920725827488,0.7113872050498472,0.15173721313476562,14.4694,177.823,5.598,0.24640497473766032,89488
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