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 414
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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411.0,0.3474225227168248,0.7226331728143772,0.14729245007038116,14.5997,176.236,5.548,0.2592304702681695,270438
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412.0,0.3473827495772548,0.7223711647304141,0.14726680517196655,14.3738,179.006,5.635,0.25845316750874464,271096
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| 414 |
413.0,0.3480236325038084,0.7226740726046419,0.1472441554069519,14.6252,175.929,5.538,0.25845316750874464,271754
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411.0,0.3474225227168248,0.7226331728143772,0.14729245007038116,14.5997,176.236,5.548,0.2592304702681695,270438
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| 413 |
412.0,0.3473827495772548,0.7223711647304141,0.14726680517196655,14.3738,179.006,5.635,0.25845316750874464,271096
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| 414 |
413.0,0.3480236325038084,0.7226740726046419,0.1472441554069519,14.6252,175.929,5.538,0.25845316750874464,271754
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414.0,0.34611753806795065,0.7219070369264458,0.147226482629776,14.3606,179.171,5.64,0.26000777302759426,272412
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415.0,0.3468202826759508,0.722459521207907,0.1472453624010086,14.3409,179.416,5.648,0.2592304702681695,273070
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