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 573
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 571 |
570.0,0.3502547591771642,0.7239634993056934,0.14664824306964874,14.6143,176.061,5.543,0.26117372716673143,375060
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| 572 |
571.0,0.35047966090507987,0.7238029146426093,0.14662781357765198,14.3402,179.426,5.648,0.2592304702681695,375718
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| 573 |
572.0,0.3502281712913851,0.7238435576732182,0.1466028094291687,14.553,176.802,5.566,0.26117372716673143,376376
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| 571 |
570.0,0.3502547591771642,0.7239634993056934,0.14664824306964874,14.6143,176.061,5.543,0.26117372716673143,375060
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| 572 |
571.0,0.35047966090507987,0.7238029146426093,0.14662781357765198,14.3402,179.426,5.648,0.2592304702681695,375718
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| 573 |
572.0,0.3502281712913851,0.7238435576732182,0.1466028094291687,14.553,176.802,5.566,0.26117372716673143,376376
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| 574 |
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573.0,0.350134077984241,0.7239738251041047,0.14662286639213562,14.6399,175.752,5.533,0.26000777302759426,377034
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