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 2
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second,eval_steps_per_second,eval_subset_accuracy,step
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1.0,0.07219894765752088,0.4952816012502254,0.272717148065567,15.8417,162.42,5.113,0.10532452390205985,658
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epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second,eval_steps_per_second,eval_subset_accuracy,step
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1.0,0.07219894765752088,0.4952816012502254,0.272717148065567,15.8417,162.42,5.113,0.10532452390205985,658
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2.0,0.09064480010203674,0.544629877304181,0.22911879420280457,14.4231,178.394,5.616,0.11232024873688301,1316
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