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 382
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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379.0,0.3436758104008564,0.7211026521371349,0.14746162295341492,14.6841,175.224,5.516,0.25806451612903225,249382
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| 381 |
380.0,0.34424721585462986,0.7210283388753784,0.14756938815116882,14.459,177.952,5.602,0.2592304702681695,250040
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| 382 |
381.0,0.34535386707880056,0.7217753946976467,0.1474810242652893,14.6123,176.085,5.543,0.2592304702681695,250698
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| 380 |
379.0,0.3436758104008564,0.7211026521371349,0.14746162295341492,14.6841,175.224,5.516,0.25806451612903225,249382
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| 381 |
380.0,0.34424721585462986,0.7210283388753784,0.14756938815116882,14.459,177.952,5.602,0.2592304702681695,250040
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| 382 |
381.0,0.34535386707880056,0.7217753946976467,0.1474810242652893,14.6123,176.085,5.543,0.2592304702681695,250698
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| 383 |
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382.0,0.3435102446968127,0.7213685414491023,0.14747925102710724,14.4661,177.865,5.599,0.25806451612903225,251356
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