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 48
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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45.0,0.2655991083531059,0.688909389093891,0.16015583276748657,14.3258,179.606,5.654,0.21842207539836767,29610
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46.0,0.267277713965834,0.6893765024806915,0.15995058417320251,14.4943,177.518,5.588,0.21919937815779247,30268
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47.0,0.2675874035520293,0.6899018806214228,0.15987393260002136,14.4997,177.452,5.586,0.21842207539836767,30926
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| 46 |
45.0,0.2655991083531059,0.688909389093891,0.16015583276748657,14.3258,179.606,5.654,0.21842207539836767,29610
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| 47 |
46.0,0.267277713965834,0.6893765024806915,0.15995058417320251,14.4943,177.518,5.588,0.21919937815779247,30268
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| 48 |
47.0,0.2675874035520293,0.6899018806214228,0.15987393260002136,14.4997,177.452,5.586,0.21842207539836767,30926
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48.0,0.26953305755046325,0.6907347916985366,0.15969185531139374,14.4501,178.061,5.605,0.21997668091721725,31584
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