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 593
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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590.0,0.35081395762441253,0.7240832507433103,0.14655527472496033,14.6562,175.557,5.527,0.26039642440730665,388220
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| 592 |
591.0,0.35099257409632867,0.7243205781891986,0.14661450684070587,14.576,176.523,5.557,0.25961912164788187,388878
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| 593 |
592.0,0.349604564348603,0.7234464959301171,0.14650070667266846,14.3493,179.312,5.645,0.25961912164788187,389536
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| 591 |
590.0,0.35081395762441253,0.7240832507433103,0.14655527472496033,14.6562,175.557,5.527,0.26039642440730665,388220
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| 592 |
591.0,0.35099257409632867,0.7243205781891986,0.14661450684070587,14.576,176.523,5.557,0.25961912164788187,388878
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| 593 |
592.0,0.349604564348603,0.7234464959301171,0.14650070667266846,14.3493,179.312,5.645,0.25961912164788187,389536
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| 594 |
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593.0,0.35040104102858166,0.7238728237686622,0.14654706418514252,14.4826,177.661,5.593,0.2592304702681695,390194
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