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 188
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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185.0,0.3297651364494541,0.7147852147852148,0.15008726716041565,14.3177,179.708,5.657,0.2510687912942091,121730
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186.0,0.32849574249626823,0.7149497675813465,0.15002425014972687,14.3642,179.126,5.639,0.2518460940536339,122388
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| 188 |
187.0,0.32880491634561565,0.714627818798643,0.15009623765945435,14.3588,179.193,5.641,0.2483482316362223,123046
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| 186 |
185.0,0.3297651364494541,0.7147852147852148,0.15008726716041565,14.3177,179.708,5.657,0.2510687912942091,121730
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| 187 |
186.0,0.32849574249626823,0.7149497675813465,0.15002425014972687,14.3642,179.126,5.639,0.2518460940536339,122388
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| 188 |
187.0,0.32880491634561565,0.714627818798643,0.15009623765945435,14.3588,179.193,5.641,0.2483482316362223,123046
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188.0,0.3302448535942163,0.7156705976414152,0.14998352527618408,14.5672,176.629,5.56,0.2549553050913331,123704
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