Instructions to use tweettemposhift/topic-topic_random3_seed0-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_seed0-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_seed0-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tweettemposhift/topic-topic_random3_seed0-roberta-base") model = AutoModelForSequenceClassification.from_pretrained("tweettemposhift/topic-topic_random3_seed0-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download summary.json from tweettemposhift/topic-topic_random3_seed0-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 267 Bytes
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https://huggingface.co/tweettemposhift/topic-topic_random3_seed0-roberta-base/resolve/main/summary.json
- Command line
-
hf download hf://tweettemposhift/topic-topic_random3_seed0-roberta-base/summary.json
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curl -L -o summary.json https://huggingface.co/tweettemposhift/topic-topic_random3_seed0-roberta-base/resolve/main/summary.json
267 Bytes
| {"test/eval_loss": 0.1348286271095276, "test/eval_f1": 0.6654411764705881, "test/eval_f1_macro": 0.29814486629496495, "test/eval_accuracy": 0.4834123222748815, "test/eval_runtime": 2.2035, "test/eval_samples_per_second": 191.515, "test/eval_steps_per_second": 12.253} |