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 140
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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137.0,0.3235181467613021,0.7121166716611728,0.15168677270412445,14.3392,179.438,5.649,0.24290711232024872,90146
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138.0,0.32201791860907525,0.7120991619410849,0.15151862800121307,14.539,176.972,5.571,0.2483482316362223,90804
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| 140 |
139.0,0.32286133886271084,0.7120235776012788,0.15163671970367432,14.4199,178.434,5.617,0.2456276719782355,91462
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| 138 |
137.0,0.3235181467613021,0.7121166716611728,0.15168677270412445,14.3392,179.438,5.649,0.24290711232024872,90146
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| 139 |
138.0,0.32201791860907525,0.7120991619410849,0.15151862800121307,14.539,176.972,5.571,0.2483482316362223,90804
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| 140 |
139.0,0.32286133886271084,0.7120235776012788,0.15163671970367432,14.4199,178.434,5.617,0.2456276719782355,91462
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140.0,0.3218010479529188,0.7117252428156604,0.15159296989440918,14.5351,177.02,5.573,0.2456276719782355,92120
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