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 310
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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307.0,0.34172907168852745,0.72041353944033,0.1481139212846756,14.4852,177.63,5.592,0.25573260785075785,202006
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308.0,0.3422183518813272,0.7206115965051628,0.14812077581882477,14.5712,176.582,5.559,0.25612125923047024,202664
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| 310 |
309.0,0.342072756005379,0.7197572622363708,0.14814136922359467,14.482,177.669,5.593,0.25612125923047024,203322
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| 308 |
307.0,0.34172907168852745,0.72041353944033,0.1481139212846756,14.4852,177.63,5.592,0.25573260785075785,202006
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| 309 |
308.0,0.3422183518813272,0.7206115965051628,0.14812077581882477,14.5712,176.582,5.559,0.25612125923047024,202664
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| 310 |
309.0,0.342072756005379,0.7197572622363708,0.14814136922359467,14.482,177.669,5.593,0.25612125923047024,203322
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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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