Instructions to use tweettemposhift/topic-topic_random3_seed1-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tweettemposhift/topic-topic_random3_seed1-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tweettemposhift/topic-topic_random3_seed1-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tweettemposhift/topic-topic_random3_seed1-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("tweettemposhift/topic-topic_random3_seed1-roberta-base") - Notebooks
- Google Colab
- Kaggle
commit files to HF hub
Browse files- summary.json +1 -0
- training_args.bin +3 -0
summary.json
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{"test/eval_loss": 0.14349016547203064, "test/eval_f1": 0.6382153249272551, "test/eval_f1_macro": 0.21198018142748706, "test/eval_accuracy": 0.42890995260663506, "test/eval_runtime": 1.8881, "test/eval_samples_per_second": 223.507, "test/eval_steps_per_second": 14.3}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:dafb96af55800f30724f0454a73cb174cec9bbd4c71e1e93702189b73f9b8705
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size 4536
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