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 178
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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175.0,0.32849063518762267,0.7143428285857072,0.15040242671966553,14.1824,181.422,5.711,0.2499028371550719,115150
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176.0,0.32770050336426393,0.7144141444411147,0.15034525096416473,14.6488,175.646,5.529,0.25068013991449667,115808
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| 178 |
177.0,0.32973337804756464,0.7145774191941829,0.15041187405586243,14.4406,178.178,5.609,0.24873688301593472,116466
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175.0,0.32849063518762267,0.7143428285857072,0.15040242671966553,14.1824,181.422,5.711,0.2499028371550719,115150
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| 177 |
176.0,0.32770050336426393,0.7144141444411147,0.15034525096416473,14.6488,175.646,5.529,0.25068013991449667,115808
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| 178 |
177.0,0.32973337804756464,0.7145774191941829,0.15041187405586243,14.4406,178.178,5.609,0.24873688301593472,116466
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178.0,0.3290397743874102,0.7146419309794534,0.15037600696086884,14.4226,178.4,5.616,0.2483482316362223,117124
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