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 313
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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310.0,0.34335916003257305,0.7211080222398729,0.1480908840894699,14.6675,175.422,5.522,0.25534395647104546,203980
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311.0,0.34349492701058315,0.720734126984127,0.14805370569229126,14.2649,180.372,5.678,0.25534395647104546,204638
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312.0,0.3428882313942803,0.7203697261839686,0.14800423383712769,14.4871,177.607,5.591,0.2545666537116207,205296
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310.0,0.34335916003257305,0.7211080222398729,0.1480908840894699,14.6675,175.422,5.522,0.25534395647104546,203980
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311.0,0.34349492701058315,0.720734126984127,0.14805370569229126,14.2649,180.372,5.678,0.25534395647104546,204638
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| 313 |
312.0,0.3428882313942803,0.7203697261839686,0.14800423383712769,14.4871,177.607,5.591,0.2545666537116207,205296
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313.0,0.34347583466469334,0.7209094519459889,0.14802765846252441,14.5061,177.374,5.584,0.2549553050913331,205954
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