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 569
Browse files- model.safetensors +1 -1
- training_log.csv +1 -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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| 567 |
566.0,0.350350841848224,0.7238435576732182,0.1465800255537033,14.7349,174.62,5.497,0.26039642440730665,372428
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| 568 |
567.0,0.3501163978902668,0.723851030110935,0.1466645449399948,14.4293,178.318,5.614,0.2592304702681695,373086
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| 569 |
568.0,0.3506598456425815,0.7242745369911855,0.14666372537612915,14.4993,177.457,5.586,0.26000777302759426,373744
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| 567 |
566.0,0.350350841848224,0.7238435576732182,0.1465800255537033,14.7349,174.62,5.497,0.26039642440730665,372428
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| 568 |
567.0,0.3501163978902668,0.723851030110935,0.1466645449399948,14.4293,178.318,5.614,0.2592304702681695,373086
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| 569 |
568.0,0.3506598456425815,0.7242745369911855,0.14666372537612915,14.4993,177.457,5.586,0.26000777302759426,373744
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| 570 |
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569.0,0.35024395251583357,0.7237821212421868,0.14658403396606445,14.5289,177.095,5.575,0.26039642440730665,374402
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