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 198
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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195.0,0.33043089849103635,0.7162911525203577,0.14982862770557404,14.4205,178.427,5.617,0.2522347454333463,128310
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196.0,0.3296198642366687,0.7154983023766727,0.14979282021522522,14.3746,178.996,5.635,0.2518460940536339,128968
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| 198 |
197.0,0.3302113385460995,0.7159113595528049,0.1497601568698883,14.2587,180.452,5.681,0.25301204819277107,129626
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| 196 |
195.0,0.33043089849103635,0.7162911525203577,0.14982862770557404,14.4205,178.427,5.617,0.2522347454333463,128310
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| 197 |
196.0,0.3296198642366687,0.7154983023766727,0.14979282021522522,14.3746,178.996,5.635,0.2518460940536339,128968
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| 198 |
197.0,0.3302113385460995,0.7159113595528049,0.1497601568698883,14.2587,180.452,5.681,0.25301204819277107,129626
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198.0,0.3307740070993921,0.7159742656226622,0.14978894591331482,14.5981,176.256,5.549,0.2522347454333463,130284
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