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 355
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3c8e7763e0edd6d99d35d871b36d6a0d5d41e933cf92e64c9a2f98ed096a7ecb
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training_log.csv
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352.0,0.34443372232872094,0.7212187375942835,0.14769645035266876,14.4889,177.584,5.59,0.25806451612903225,231616
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| 354 |
353.0,0.3439576446107072,0.7208227753763601,0.14767196774482727,14.4397,178.189,5.61,0.25728721336960747,232274
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| 355 |
354.0,0.34615142394910225,0.7217322366465166,0.14770740270614624,14.4791,177.704,5.594,0.2565099106101827,232932
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| 353 |
352.0,0.34443372232872094,0.7212187375942835,0.14769645035266876,14.4889,177.584,5.59,0.25806451612903225,231616
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| 354 |
353.0,0.3439576446107072,0.7208227753763601,0.14767196774482727,14.4397,178.189,5.61,0.25728721336960747,232274
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| 355 |
354.0,0.34615142394910225,0.7217322366465166,0.14770740270614624,14.4791,177.704,5.594,0.2565099106101827,232932
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| 356 |
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355.0,0.3439351685538685,0.7213440699870762,0.14758044481277466,14.2935,180.012,5.667,0.25767586474931986,233590
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