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 448
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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training_log.csv
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445.0,0.34865802497593484,0.7225396194545184,0.14700216054916382,14.7274,174.709,5.5,0.2592304702681695,292810
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| 447 |
446.0,0.34834989858767434,0.7227767019216446,0.14704295992851257,14.4411,178.172,5.609,0.2592304702681695,293468
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| 448 |
447.0,0.349032471473642,0.7228784119106699,0.14702600240707397,14.4264,178.353,5.615,0.25884181888845703,294126
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| 446 |
445.0,0.34865802497593484,0.7225396194545184,0.14700216054916382,14.7274,174.709,5.5,0.2592304702681695,292810
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| 447 |
446.0,0.34834989858767434,0.7227767019216446,0.14704295992851257,14.4411,178.172,5.609,0.2592304702681695,293468
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| 448 |
447.0,0.349032471473642,0.7228784119106699,0.14702600240707397,14.4264,178.353,5.615,0.25884181888845703,294126
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| 449 |
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448.0,0.34743373605920497,0.7229501687512409,0.14702177047729492,14.6357,175.803,5.534,0.25961912164788187,294784
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| 450 |
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449.0,0.3484763826761565,0.7228258173339287,0.14709262549877167,14.3419,179.405,5.648,0.2592304702681695,295442
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