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 105
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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102.0,0.3094651614387574,0.7068809643395279,0.15355069935321808,14.569,176.608,5.56,0.24251846094053633,67116
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103.0,0.30961560666250765,0.7070696927093794,0.153495192527771,14.5541,176.788,5.565,0.24329576369996114,67774
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| 105 |
104.0,0.3107585509994827,0.7074187553367823,0.1533646285533905,14.5541,176.789,5.565,0.24251846094053633,68432
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102.0,0.3094651614387574,0.7068809643395279,0.15355069935321808,14.569,176.608,5.56,0.24251846094053633,67116
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| 104 |
103.0,0.30961560666250765,0.7070696927093794,0.153495192527771,14.5541,176.788,5.565,0.24329576369996114,67774
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| 105 |
104.0,0.3107585509994827,0.7074187553367823,0.1533646285533905,14.5541,176.789,5.565,0.24251846094053633,68432
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105.0,0.3110719167859119,0.7078979099678456,0.15329378843307495,14.5299,177.084,5.575,0.24446171783909834,69090
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