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 228
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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225.0,0.33766902859853326,0.7175656158175208,0.1492282748222351,14.5953,176.289,5.55,0.25068013991449667,148050
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227.0,0.33742227248481743,0.7179818887451488,0.14922663569450378,14.443,178.149,5.608,0.2522347454333463,149366
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225.0,0.33766902859853326,0.7175656158175208,0.1492282748222351,14.5953,176.289,5.55,0.25068013991449667,148050
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| 228 |
227.0,0.33742227248481743,0.7179818887451488,0.14922663569450378,14.443,178.149,5.608,0.2522347454333463,149366
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228.0,0.33909697723730925,0.7179843959648163,0.149189755320549,14.3461,179.352,5.646,0.2522347454333463,150024
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