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 239
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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236.0,0.3374719033850877,0.7178720860729229,0.1490362584590912,14.5985,176.251,5.549,0.2518460940536339,155288
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237.0,0.33692668083185146,0.7179615039393638,0.1490037590265274,14.2898,180.058,5.668,0.25340069957248346,155946
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| 239 |
238.0,0.3366017888109203,0.7174097135740971,0.14901141822338104,14.3412,179.413,5.648,0.2526233968130587,156604
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236.0,0.3374719033850877,0.7178720860729229,0.1490362584590912,14.5985,176.251,5.549,0.2518460940536339,155288
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| 238 |
237.0,0.33692668083185146,0.7179615039393638,0.1490037590265274,14.2898,180.058,5.668,0.25340069957248346,155946
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| 239 |
238.0,0.3366017888109203,0.7174097135740971,0.14901141822338104,14.3412,179.413,5.648,0.2526233968130587,156604
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239.0,0.3377406924458641,0.7178365480214755,0.14904816448688507,14.4407,178.177,5.609,0.2510687912942091,157262
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