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 484
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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oid sha256:5e5a4fb6037382d4fa18a7567f337286212a15c54278221bfc4d207b1b3437e1
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training_log.csv
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| 482 |
481.0,0.34832383975552805,0.7225037257824143,0.14686325192451477,14.3457,179.356,5.646,0.2592304702681695,316498
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| 483 |
482.0,0.3479793026246557,0.7225947449461084,0.14685294032096863,14.6459,175.681,5.531,0.26000777302759426,317156
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| 484 |
483.0,0.34894194826720565,0.7231700059512002,0.14691896736621857,14.2602,180.432,5.68,0.2592304702681695,317814
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| 482 |
481.0,0.34832383975552805,0.7225037257824143,0.14686325192451477,14.3457,179.356,5.646,0.2592304702681695,316498
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| 483 |
482.0,0.3479793026246557,0.7225947449461084,0.14685294032096863,14.6459,175.681,5.531,0.26000777302759426,317156
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| 484 |
483.0,0.34894194826720565,0.7231700059512002,0.14691896736621857,14.2602,180.432,5.68,0.2592304702681695,317814
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| 485 |
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484.0,0.3494332508106975,0.7230647239567847,0.14693225920200348,14.3418,179.405,5.648,0.25845316750874464,318472
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| 486 |
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485.0,0.34972162046302896,0.7233387921194978,0.1468595266342163,14.5756,176.528,5.557,0.2592304702681695,319130
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