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 114
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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111.0,0.3156943232261125,0.7088950137810073,0.15296627581119537,14.4864,177.615,5.591,0.24407306645938592,73038
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| 113 |
112.0,0.31534290534718096,0.7091864831038799,0.1529737263917923,14.554,176.79,5.565,0.24135250680139914,73696
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| 114 |
113.0,0.31515174515827354,0.7087349397590361,0.15278805792331696,14.4559,177.99,5.603,0.24329576369996114,74354
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| 112 |
111.0,0.3156943232261125,0.7088950137810073,0.15296627581119537,14.4864,177.615,5.591,0.24407306645938592,73038
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| 113 |
112.0,0.31534290534718096,0.7091864831038799,0.1529737263917923,14.554,176.79,5.565,0.24135250680139914,73696
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| 114 |
113.0,0.31515174515827354,0.7087349397590361,0.15278805792331696,14.4559,177.99,5.603,0.24329576369996114,74354
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| 115 |
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114.0,0.31434438502919576,0.7090717405492244,0.1527528017759323,14.4357,178.239,5.611,0.24485036921881073,75012
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