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 126
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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| 124 |
123.0,0.3168395729202393,0.709751702042451,0.15236599743366241,14.5374,176.992,5.572,0.24251846094053633,80934
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| 125 |
124.0,0.3201488920534683,0.710434348477284,0.1523277908563614,14.6091,176.123,5.544,0.24368441507967353,81592
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| 126 |
125.0,0.318394773442475,0.7103983963918817,0.1521897166967392,14.6778,175.298,5.519,0.24485036921881073,82250
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| 124 |
123.0,0.3168395729202393,0.709751702042451,0.15236599743366241,14.5374,176.992,5.572,0.24251846094053633,80934
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| 125 |
124.0,0.3201488920534683,0.710434348477284,0.1523277908563614,14.6091,176.123,5.544,0.24368441507967353,81592
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| 126 |
125.0,0.318394773442475,0.7103983963918817,0.1521897166967392,14.6778,175.298,5.519,0.24485036921881073,82250
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| 127 |
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126.0,0.32028803924651456,0.7103775943985996,0.15225693583488464,14.2393,180.698,5.688,0.24329576369996114,82908
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