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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 580
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 578 |
577.0,0.35003164166062223,0.7238916988991372,0.1466130167245865,14.3435,179.385,5.647,0.26078507578701904,379666
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| 579 |
578.0,0.350186387320148,0.7238576667657846,0.14656561613082886,14.6309,175.86,5.536,0.2592304702681695,380324
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| 580 |
579.0,0.3497931758468584,0.7237755304382312,0.14662408828735352,14.5149,177.266,5.58,0.26039642440730665,380982
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| 578 |
577.0,0.35003164166062223,0.7238916988991372,0.1466130167245865,14.3435,179.385,5.647,0.26078507578701904,379666
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| 579 |
578.0,0.350186387320148,0.7238576667657846,0.14656561613082886,14.6309,175.86,5.536,0.2592304702681695,380324
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| 580 |
579.0,0.3497931758468584,0.7237755304382312,0.14662408828735352,14.5149,177.266,5.58,0.26039642440730665,380982
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| 581 |
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580.0,0.3498016783996152,0.7237188073622067,0.14655892550945282,14.4433,178.145,5.608,0.26000777302759426,381640
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