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 430
Browse files- model.safetensors +1 -1
- training_log.csv +2 -0
model.safetensors
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training_log.csv
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427.0,0.34845194120888195,0.7230219398391741,0.1471378058195114,14.4629,177.903,5.601,0.25961912164788187,280966
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| 429 |
428.0,0.3479095064570868,0.7227290757635859,0.14716045558452606,14.5167,177.244,5.58,0.25845316750874464,281624
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| 430 |
429.0,0.34794666274185093,0.7225255633872729,0.14715322852134705,14.5111,177.313,5.582,0.25884181888845703,282282
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| 428 |
427.0,0.34845194120888195,0.7230219398391741,0.1471378058195114,14.4629,177.903,5.601,0.25961912164788187,280966
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| 429 |
428.0,0.3479095064570868,0.7227290757635859,0.14716045558452606,14.5167,177.244,5.58,0.25845316750874464,281624
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| 430 |
429.0,0.34794666274185093,0.7225255633872729,0.14715322852134705,14.5111,177.313,5.582,0.25884181888845703,282282
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| 431 |
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430.0,0.34799312092006496,0.7229967749937981,0.14717139303684235,14.2355,180.746,5.69,0.25845316750874464,282940
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| 432 |
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431.0,0.347565659255218,0.72248708780294,0.1471419632434845,14.6265,175.913,5.538,0.25961912164788187,283598
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