Uploading JMVAE in asenella/reproduce_jmvae_seed_0
Browse files- README.md +13 -0
- decoders.pkl +3 -0
- encoders.pkl +3 -0
- joint_encoder.pkl +3 -0
- model.pt +3 -0
- model_config.json +1 -0
README.md
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---
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language: en
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tags:
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- multivae
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license: apache-2.0
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---
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### Downloading this model from the Hub
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This model was trained with multivae. It can be downloaded or reloaded using the method `load_from_hf_hub`
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```python
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>>> from multivae.models import AutoModel
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>>> model = AutoModel.load_from_hf_hub(hf_hub_path="your_hf_username/repo_name")
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```
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decoders.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:42ee368258e21bbf588981cc522ab5b49a673818ab12548ed4b4cefe76218618
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size 4020768
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encoders.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:86fbc12e6968a79fbad7112f25b6ee0486b3398cd862e4988605b3fc5d4cb2bf
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size 4283434
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joint_encoder.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:8c8990c8578221c673ecb017f4037e19d23a468dd9f381da169b97fcd4dbc2fa
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size 4008176
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model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd2f3484b4a958c1c903dd5c39e717461ddd03c128119b3821b9bd30e13ca38f
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size 12257571
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model_config.json
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{"name": "JMVAEConfig", "n_modalities": 2, "latent_dim": 64, "input_dims": {"images": [1, 28, 28], "labels": [1]}, "uses_likelihood_rescaling": false, "rescale_factors": null, "decoders_dist": {"images": "bernoulli", "labels": "categorical"}, "decoder_dist_params": {}, "custom_architectures": ["encoders", "decoders", "joint_encoder"], "alpha": 0.1, "warmup": 200}
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