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 51
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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48.0,0.26953305755046325,0.6907347916985366,0.15969185531139374,14.4501,178.061,5.605,0.21997668091721725,31584
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49.0,0.26942695457279886,0.6913429183236464,0.15950597822666168,14.314,179.754,5.659,0.21997668091721725,32242
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| 51 |
50.0,0.2701102842045285,0.6911112244377582,0.15923741459846497,14.4937,177.526,5.589,0.22230858919549165,32900
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48.0,0.26953305755046325,0.6907347916985366,0.15969185531139374,14.4501,178.061,5.605,0.21997668091721725,31584
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| 50 |
49.0,0.26942695457279886,0.6913429183236464,0.15950597822666168,14.314,179.754,5.659,0.21997668091721725,32242
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| 51 |
50.0,0.2701102842045285,0.6911112244377582,0.15923741459846497,14.4937,177.526,5.589,0.22230858919549165,32900
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51.0,0.27376616242553664,0.6929029630383872,0.15914605557918549,14.6599,175.513,5.525,0.22153128643606684,33558
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