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 499
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 497 |
496.0,0.3500768425714955,0.7239908757314292,0.1468363255262375,14.2729,180.271,5.675,0.26039642440730665,326368
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| 498 |
497.0,0.34985561345308214,0.7235667526284467,0.14682678878307343,14.4859,177.621,5.592,0.2592304702681695,327026
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| 499 |
498.0,0.34961635834937443,0.7234993297254356,0.1467776894569397,14.4833,177.652,5.593,0.25961912164788187,327684
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| 497 |
496.0,0.3500768425714955,0.7239908757314292,0.1468363255262375,14.2729,180.271,5.675,0.26039642440730665,326368
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| 498 |
497.0,0.34985561345308214,0.7235667526284467,0.14682678878307343,14.4859,177.621,5.592,0.2592304702681695,327026
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| 499 |
498.0,0.34961635834937443,0.7234993297254356,0.1467776894569397,14.4833,177.652,5.593,0.25961912164788187,327684
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| 500 |
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499.0,0.3495512712475295,0.7235562611629291,0.14683789014816284,14.4621,177.914,5.601,0.26000777302759426,328342
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