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 24
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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21.0,0.2173037125153878,0.6656912948061449,0.16946576535701752,14.278,180.208,5.673,0.18771861640108822,13818
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23.0,0.2228445020395818,0.6689608425882476,0.16809335350990295,14.4792,177.703,5.594,0.1916051301982122,15134
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21.0,0.2173037125153878,0.6656912948061449,0.16946576535701752,14.278,180.208,5.673,0.18771861640108822,13818
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| 24 |
23.0,0.2228445020395818,0.6689608425882476,0.16809335350990295,14.4792,177.703,5.594,0.1916051301982122,15134
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24.0,0.22818936455009958,0.6706499714241181,0.1676853448152542,14.4154,178.49,5.619,0.190439176059075,15792
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