# Repeating eval-F1 scores with seed + data randomization

**URL:** <https://discuss.huggingface.co/t/repeating-eval-f1-scores-with-seed-data-randomization/161377>\
**Category:** Intermediate\
**Created:** [July 1, 2025, 7:31pm UTC](https://discuss.huggingface.co/t/repeating-eval-f1-scores-with-seed-data-randomization/161377 "2025-07-01T19:31:53Z")\
**Posts on this page:** 1\
**Showing post:** 3

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [July 1, 2025, 9:00pm UTC](https://discuss.huggingface.co/t/repeating-eval-f1-scores-with-seed-data-randomization/161377/3 "2025-07-01T21:00:16Z")

</div>

I wonder if the model output has reached the ideal value…

> [@Same metrics after every epoch](https://discuss.huggingface.co/t/same-metrics-after-every-epoch/88851):
>
> This is a duplicate of a question I’ve posted on Stack Overflow that until now has gotten no answers (link to [SO post](https://stackoverflow.com/questions/78492018/hugging-face-trainer-object-returning-constant-values-for-metrics-every-epoch)). Basically, I’m using the Trainer class to fine tune a model on a text classification task and the metrics I’m tracking are taking on the same values after every epoch - changing LR and batch size does not seem to change anything. This is the code I’m currently using: def compute\_metrics(eval\_preds): logits, labels = eval\_preds preds = np.argmax(logits, axis=-1) …

> **[F1 - a Hugging Face Space by evaluate-metric](https://huggingface.co/spaces/evaluate-metric/f1)**
>
> The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation: F1 = 2 \* (precision \* recall) / (precision + recall)

---

_[View the full topic](https://discuss.huggingface.co/t/repeating-eval-f1-scores-with-seed-data-randomization/161377)._