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 233
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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230.0,0.33681685141736334,0.7178158628935831,0.14904747903347015,14.4458,178.114,5.607,0.2541780023319083,151340
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231.0,0.3363482042187757,0.7176517571884984,0.1490131914615631,14.4666,177.858,5.599,0.25534395647104546,151998
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232.0,0.3364679099816322,0.7179027113237639,0.14909330010414124,14.3932,178.765,5.628,0.25534395647104546,152656
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230.0,0.33681685141736334,0.7178158628935831,0.14904747903347015,14.4458,178.114,5.607,0.2541780023319083,151340
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231.0,0.3363482042187757,0.7176517571884984,0.1490131914615631,14.4666,177.858,5.599,0.25534395647104546,151998
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| 233 |
232.0,0.3364679099816322,0.7179027113237639,0.14909330010414124,14.3932,178.765,5.628,0.25534395647104546,152656
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233.0,0.33845098697251264,0.7186893083013794,0.14911434054374695,14.3433,179.386,5.647,0.25534395647104546,153314
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