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 161
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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158.0,0.32507899057575806,0.7136850535589148,0.15080325305461884,14.1423,181.936,5.727,0.2483482316362223,103964
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159.0,0.3272594996873891,0.7137862137862138,0.1508711278438568,14.4905,177.565,5.59,0.24757092887679752,104622
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160.0,0.32382046266571274,0.7133566783391696,0.15076349675655365,14.4192,178.442,5.618,0.2499028371550719,105280
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| 159 |
158.0,0.32507899057575806,0.7136850535589148,0.15080325305461884,14.1423,181.936,5.727,0.2483482316362223,103964
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| 160 |
159.0,0.3272594996873891,0.7137862137862138,0.1508711278438568,14.4905,177.565,5.59,0.24757092887679752,104622
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| 161 |
160.0,0.32382046266571274,0.7133566783391696,0.15076349675655365,14.4192,178.442,5.618,0.2499028371550719,105280
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161.0,0.3279552991064018,0.7137939635819406,0.15076573193073273,14.5126,177.294,5.581,0.2491255343956471,105938
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