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 418
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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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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| 417 |
416.0,0.34717869123257517,0.7225806451612903,0.14726519584655762,14.6447,175.695,5.531,0.2592304702681695,273728
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| 418 |
417.0,0.34675744344335757,0.7226139636508094,0.14723117649555206,14.3354,179.485,5.65,0.2592304702681695,274386
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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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| 417 |
416.0,0.34717869123257517,0.7225806451612903,0.14726519584655762,14.6447,175.695,5.531,0.2592304702681695,273728
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| 418 |
417.0,0.34675744344335757,0.7226139636508094,0.14723117649555206,14.3354,179.485,5.65,0.2592304702681695,274386
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| 419 |
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418.0,0.3464682371697305,0.7225812866660037,0.14720655977725983,14.401,178.668,5.625,0.26000777302759426,275044
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419.0,0.34628945923764703,0.722255369928401,0.14717763662338257,14.6114,176.095,5.544,0.2592304702681695,275702
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