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 33
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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30.0,0.24228681292669593,0.677744991458301,0.16465836763381958,14.4736,177.772,5.596,0.20481927710843373,19740
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31.0,0.24679782639046557,0.6796416434970651,0.1643495112657547,14.261,180.422,5.68,0.20443062572872134,20398
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32.0,0.24730023686989264,0.6798394566224143,0.1640038788318634,14.6112,176.098,5.544,0.20481927710843373,21056
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30.0,0.24228681292669593,0.677744991458301,0.16465836763381958,14.4736,177.772,5.596,0.20481927710843373,19740
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| 32 |
31.0,0.24679782639046557,0.6796416434970651,0.1643495112657547,14.261,180.422,5.68,0.20443062572872134,20398
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| 33 |
32.0,0.24730023686989264,0.6798394566224143,0.1640038788318634,14.6112,176.098,5.544,0.20481927710843373,21056
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33.0,0.24892916625834052,0.6807284700072024,0.16366547346115112,14.4337,178.263,5.612,0.20365332296929653,21714
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