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 518
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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515.0,0.3495831549123265,0.7235966219572777,0.14676524698734283,14.4953,177.505,5.588,0.26117372716673143,338870
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| 517 |
516.0,0.3486360353420008,0.72343175265931,0.14669719338417053,14.5521,176.813,5.566,0.26078507578701904,339528
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| 518 |
517.0,0.3503816976862009,0.7239464551313832,0.14678263664245605,14.4644,177.886,5.6,0.26039642440730665,340186
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| 516 |
515.0,0.3495831549123265,0.7235966219572777,0.14676524698734283,14.4953,177.505,5.588,0.26117372716673143,338870
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| 517 |
516.0,0.3486360353420008,0.72343175265931,0.14669719338417053,14.5521,176.813,5.566,0.26078507578701904,339528
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| 518 |
517.0,0.3503816976862009,0.7239464551313832,0.14678263664245605,14.4644,177.886,5.6,0.26039642440730665,340186
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518.0,0.3506784739885118,0.7240576551587499,0.14676901698112488,14.2653,180.368,5.678,0.26039642440730665,340844
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