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 216
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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213.0,0.3369869002542824,0.7176628676653561,0.14942659437656403,14.3432,179.388,5.647,0.2514574426739215,140154
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214.0,0.3363305286813756,0.7178054862842893,0.14936485886573792,14.4616,177.919,5.601,0.2545666537116207,140812
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215.0,0.33625532579561296,0.7175259792166268,0.149298757314682,14.4428,178.151,5.608,0.2565099106101827,141470
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213.0,0.3369869002542824,0.7176628676653561,0.14942659437656403,14.3432,179.388,5.647,0.2514574426739215,140154
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214.0,0.3363305286813756,0.7178054862842893,0.14936485886573792,14.4616,177.919,5.601,0.2545666537116207,140812
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| 216 |
215.0,0.33625532579561296,0.7175259792166268,0.149298757314682,14.4428,178.151,5.608,0.2565099106101827,141470
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216.0,0.3353741866951158,0.7168154836135082,0.14933860301971436,14.3731,179.015,5.636,0.25340069957248346,142128
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