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 545
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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542.0,0.35007817319828094,0.7236574602072693,0.14671221375465393,14.6095,176.118,5.544,0.2592304702681695,356636
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| 544 |
543.0,0.3501438529578002,0.7237991266375546,0.14667768776416779,14.3272,179.588,5.654,0.2592304702681695,357294
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| 545 |
544.0,0.3506622004882289,0.7243414851927179,0.14667896926403046,14.5686,176.613,5.56,0.26039642440730665,357952
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| 543 |
542.0,0.35007817319828094,0.7236574602072693,0.14671221375465393,14.6095,176.118,5.544,0.2592304702681695,356636
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| 544 |
543.0,0.3501438529578002,0.7237991266375546,0.14667768776416779,14.3272,179.588,5.654,0.2592304702681695,357294
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| 545 |
544.0,0.3506622004882289,0.7243414851927179,0.14667896926403046,14.5686,176.613,5.56,0.26039642440730665,357952
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| 546 |
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545.0,0.34996722509154354,0.7238965294672558,0.14664900302886963,14.5012,177.434,5.586,0.26000777302759426,358610
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