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 71
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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68.0,0.2908345423657288,0.6995153473344103,0.15658311545848846,14.3703,179.05,5.637,0.23047026816945201,44744
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69.0,0.2928437881019003,0.6998886977638369,0.15640489757061005,14.5719,176.573,5.559,0.2312475709288768,45402
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| 71 |
70.0,0.29435499124175774,0.7003339742940998,0.15630054473876953,14.662,175.487,5.524,0.232413525068014,46060
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| 69 |
68.0,0.2908345423657288,0.6995153473344103,0.15658311545848846,14.3703,179.05,5.637,0.23047026816945201,44744
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| 70 |
69.0,0.2928437881019003,0.6998886977638369,0.15640489757061005,14.5719,176.573,5.559,0.2312475709288768,45402
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| 71 |
70.0,0.29435499124175774,0.7003339742940998,0.15630054473876953,14.662,175.487,5.524,0.232413525068014,46060
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71.0,0.29322997203558887,0.7005352994647005,0.1562827080488205,14.5302,177.079,5.575,0.232413525068014,46718
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