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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 164
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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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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162.0,0.32595538221191406,0.7133662625355486,0.15080305933952332,14.4065,178.6,5.622,0.2467936261173727,106596
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| 164 |
163.0,0.32662540982907085,0.7136365906026864,0.15070709586143494,14.5798,176.477,5.556,0.2499028371550719,107254
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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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| 163 |
162.0,0.32595538221191406,0.7133662625355486,0.15080305933952332,14.4065,178.6,5.622,0.2467936261173727,106596
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| 164 |
163.0,0.32662540982907085,0.7136365906026864,0.15070709586143494,14.5798,176.477,5.556,0.2499028371550719,107254
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164.0,0.3258130091476018,0.713399379565696,0.1506299078464508,14.6106,176.105,5.544,0.2514574426739215,107912
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