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 353
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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training_log.csv
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350.0,0.3450210283061973,0.7214026722296727,0.14763785898685455,14.5045,177.393,5.584,0.25806451612903225,230300
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| 352 |
351.0,0.34450011125772606,0.7210445315990667,0.147658571600914,14.4741,177.766,5.596,0.2565099106101827,230958
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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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| 351 |
350.0,0.3450210283061973,0.7214026722296727,0.14763785898685455,14.5045,177.393,5.584,0.25806451612903225,230300
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| 352 |
351.0,0.34450011125772606,0.7210445315990667,0.147658571600914,14.4741,177.766,5.596,0.2565099106101827,230958
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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 |
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353.0,0.3439576446107072,0.7208227753763601,0.14767196774482727,14.4397,178.189,5.61,0.25728721336960747,232274
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