Uploading PoincareVAE in clementchadebec/reproduced_wrapped_poincare_vae

README.md

@@ -2,21 +2,12 @@
 language: en
 tags:
 - pythae
-- reproducibility
 license: apache-2.0
 ---
 
+### Downloading this model from the Hub
 This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub`
 ```python
 >>> from pythae.models import AutoModel
->>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_wrapped_poincare_vae")
+>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
 ```
-
-## Reproducibility
-This trained model reproduces the results of the official implementation of [1].
-
-| Model | Dataset | Metric | Obtained value | Reference value |
-|:---:|:---:|:---:|:---:|:---:|
-| PoincareVAE | MNIST | NLL | 102.06 (0.00) | 101.47 (0.01) |
-
-[1] Mathieu, E., Le Lan, C., Maddison, C. J., Tomioka, R., & Teh, Y. W. (2019). Continuous hierarchical representations with poincaré variational auto-encoders. Advances in neural information processing systems, 32.

decoder.pkl

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encoder.pkl

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model.pt

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