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 248
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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245.0,0.33636802003257293,0.7175313327008539,0.14884239435195923,14.6555,175.565,5.527,0.25340069957248346,161210
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246.0,0.33742838394694497,0.718333664744482,0.14889340102672577,14.4552,177.999,5.604,0.2522347454333463,161868
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| 248 |
247.0,0.337936401990175,0.7187375547590601,0.14883536100387573,14.4098,178.559,5.621,0.2541780023319083,162526
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| 246 |
245.0,0.33636802003257293,0.7175313327008539,0.14884239435195923,14.6555,175.565,5.527,0.25340069957248346,161210
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| 247 |
246.0,0.33742838394694497,0.718333664744482,0.14889340102672577,14.4552,177.999,5.604,0.2522347454333463,161868
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| 248 |
247.0,0.337936401990175,0.7187375547590601,0.14883536100387573,14.4098,178.559,5.621,0.2541780023319083,162526
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248.0,0.33695777929744875,0.7183583803484946,0.14878807961940765,14.3278,179.581,5.653,0.2568985619898951,163184
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