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 91
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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88.0,0.3051466543134685,0.7051558813193434,0.15466587245464325,14.5714,176.578,5.559,0.23435678196657597,57904
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| 90 |
89.0,0.3040521044152308,0.7043346774193548,0.15452654659748077,14.4656,177.87,5.599,0.23902059852312477,58562
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| 91 |
90.0,0.3054450761127522,0.7053144954497461,0.15443642437458038,14.3998,178.683,5.625,0.23902059852312477,59220
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| 89 |
88.0,0.3051466543134685,0.7051558813193434,0.15466587245464325,14.5714,176.578,5.559,0.23435678196657597,57904
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| 90 |
89.0,0.3040521044152308,0.7043346774193548,0.15452654659748077,14.4656,177.87,5.599,0.23902059852312477,58562
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| 91 |
90.0,0.3054450761127522,0.7053144954497461,0.15443642437458038,14.3998,178.683,5.625,0.23902059852312477,59220
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| 92 |
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91.0,0.30597329371337467,0.7054607853033209,0.15424156188964844,14.4739,177.768,5.596,0.24135250680139914,59878
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