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 324
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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321.0,0.3433217724296478,0.7205209783257108,0.14795255661010742,14.4526,178.03,5.605,0.25612125923047024,211218
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322.0,0.34168609610659,0.7204456159546426,0.14789707958698273,14.1742,181.526,5.715,0.25728721336960747,211876
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| 324 |
323.0,0.3410967636533647,0.7198567449263829,0.14793498814105988,14.586,176.403,5.553,0.2565099106101827,212534
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| 322 |
321.0,0.3433217724296478,0.7205209783257108,0.14795255661010742,14.4526,178.03,5.605,0.25612125923047024,211218
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| 323 |
322.0,0.34168609610659,0.7204456159546426,0.14789707958698273,14.1742,181.526,5.715,0.25728721336960747,211876
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| 324 |
323.0,0.3410967636533647,0.7198567449263829,0.14793498814105988,14.586,176.403,5.553,0.2565099106101827,212534
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| 325 |
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324.0,0.34415262753759757,0.7207985697258641,0.14787223935127258,14.3617,179.157,5.64,0.25728721336960747,213192
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