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 154
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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151.0,0.32350235847902076,0.7122136640992298,0.1510957032442093,14.586,176.403,5.553,0.2471822774970851,99358
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152.0,0.32457056648877614,0.7123040713105313,0.151083841919899,14.608,176.137,5.545,0.2467936261173727,100016
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| 154 |
153.0,0.32528862763004934,0.7128970838761399,0.15103228390216827,14.3728,179.018,5.636,0.2502914885347843,100674
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| 152 |
151.0,0.32350235847902076,0.7122136640992298,0.1510957032442093,14.586,176.403,5.553,0.2471822774970851,99358
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| 153 |
152.0,0.32457056648877614,0.7123040713105313,0.151083841919899,14.608,176.137,5.545,0.2467936261173727,100016
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| 154 |
153.0,0.32528862763004934,0.7128970838761399,0.15103228390216827,14.3728,179.018,5.636,0.2502914885347843,100674
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| 155 |
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154.0,0.3231865683950093,0.7123698959167334,0.15099425613880157,14.5166,177.245,5.58,0.2479595802565099,101332
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