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 79
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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76.0,0.2957956067490935,0.7021801520567947,0.15572988986968994,14.4793,177.702,5.594,0.23163622230858918,50008
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| 78 |
77.0,0.2942873187016953,0.7011883691529709,0.15558689832687378,14.6081,176.135,5.545,0.23396813058686358,50666
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| 79 |
78.0,0.29758067730023174,0.7024193548387097,0.15550877153873444,14.5832,176.436,5.554,0.23319082782743877,51324
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| 77 |
76.0,0.2957956067490935,0.7021801520567947,0.15572988986968994,14.4793,177.702,5.594,0.23163622230858918,50008
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| 78 |
77.0,0.2942873187016953,0.7011883691529709,0.15558689832687378,14.6081,176.135,5.545,0.23396813058686358,50666
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| 79 |
78.0,0.29758067730023174,0.7024193548387097,0.15550877153873444,14.5832,176.436,5.554,0.23319082782743877,51324
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79.0,0.29527932273753577,0.7020242914979757,0.15526725351810455,14.4413,178.169,5.609,0.23435678196657597,51982
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