Improve model card: Add pipeline tag, library name, and clean up metadata

This PR improves the model card for `ConfRover-interp-20M-v1.0` by:

1. **Adding `pipeline_tag: other`**: This helps categorize the model correctly on the Hugging Face Hub for its specialized task of protein conformation and dynamics modeling.
2. **Adding `library_name: confrover`**: Evidence from the `get_started_code` snippet (`from confrover import ConfRover`) indicates compatibility with this library, enabling automated usage snippets on the Hub.
3. **Cleaning up the metadata YAML block**: Several descriptive fields (e.g., `model_summary`, `model_description`, `repo`, `paper`, `demo`, `get_started_code`) have been removed from the YAML block as they are already comprehensively presented in the Markdown content. This aligns with Hugging Face guidelines for concise and relevant metadata.

The Markdown content remains unchanged as it already presents the model information clearly.

README.md

@@ -1,70 +1,7 @@
 ---
 license: apache-2.0
-variant: interp
-size: 20M
-version: v1.0
-model_summary: ConfRover base model trained for conformation interpolation
-model_description: '
-
-  ConfRover is a deep generative model for protein 3D conformation and motion dynamics.
-
-  It leverages diffusion probability model to learn the distribution of protein 3D
-  conformations and captures the their temporal dependencies between frames through
-  temporal causal transformers.
-
-  Models are trained using molecular dynamics (MD) trajectories data and can generate
-  protein conformation ensembles and motion trajectories conditioned on the input
-  protein amino acid sequence.
-
-
-  This variant was continued trained from the base model with additional conformation
-  interpolation task.'
-recommend: For interpolation tasks
-model_id: ConfRover-interp-20M-v1.0
-name: ConfRover
-repo: https://github.com/ByteDance-Seed/ConfRover
-paper: https://arxiv.org/abs/2505.17478
-demo: https://ByteDance-Seed.github.io/ConfRover
-get_started_code: "\n```python\nfrom confrover import ConfRover\n\nmodel = ConfRover.from_pretrained(<model_name>)\n\
-  \nmodel.to(\"cuda\")\n\nmodel.generate(\n    case_id=<case_name>,\n    seqres=<amino_acid_sequence>,\n\
-  \    output_dir=</path/to/save/output>,\n    task_mode=<\"forward\"|\"iid\"|\"interp\"\
-  >,\n    n_replicates=<int>, # number of replicated trajectories (forward and interp)\
-  \ or total number of conformation samples (iid)\n    n_frames=<int>, # number of\
-  \ frames in the trajectory, including the conditioning frames.\n    stride_in_10ps=256,\
-  \ # time interval between frames in the unit of 10 ps.\n    conditions=..., # information\
-  \ for conditioning frames for forward simulation and interp. See `ConfRover.generate`\
-  \ for more details.\n)\n```\n"
-model_specs: '
-
-  ConfRover contains encoder, temporal module, and diffusion decoder.
-
-  - The encoder maps the input amino acid sequence (through a folding model) and coordinates
-  of context frames to a latent representation.
-
-  - The temporal module models the temporal dependencies between frames using an interleaving
-  of causal transformers (across the temporal dimension) and pairformers (to update
-  structures).
-
-  - The diffusion model learns the probability distribution of protein conformations
-  and generates samples conditioned on the input sequence and conditioning representation.
-
-  '
-bias_risks_limitations: '
-
-  ConfRover is trained on limited MD trajectories data and may not generalize well
-  to out-of-distribution data.
-
-  The quality of generated conformations is also limited by the quality of the input
-  data and the computational resources.
-
-  Currently, ConfRover only supports protein conformation generation and models the
-  coordinates of heavy atoms.
-
-  '
-citation_bibtex: "\n```text\n@article{confrover2025,\n  title={Simultaneous Modeling\
-  \ of Protein Conformation and Dynamics via Autoregression},\n  author={Shen, Yuning\
-  \ and Wang, Lihao and Yuan, Huizhuo and Wang, Yan and Yang, Bangji and Gu, Quanquan},\n\
-  \  journal={arXiv preprint arXiv:2505.17478},\n  year={2025}\n}\n```\n"
+pipeline_tag: other
+library_name: confrover
 ---
 
 # Model Card for `ConfRover-interp-20M-v1.0`
@@ -84,6 +21,7 @@ ConfRover is a deep generative model for protein 3D conformation and motion dyna
 It leverages diffusion probability model to learn the distribution of protein 3D conformations and captures the their temporal dependencies between frames through temporal causal transformers.
 Models are trained using molecular dynamics (MD) trajectories data and can generate protein conformation ensembles and motion trajectories conditioned on the input protein amino acid sequence.
 
+
 This variant was continued trained from the base model with additional conformation interpolation task.
 
 **Basic info**
@@ -149,9 +87,12 @@ ConfRover contains encoder, temporal module, and diffusion decoder.
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 
-ConfRover is trained on limited MD trajectories data and may not generalize well to out-of-distribution data.
-The quality of generated conformations is also limited by the quality of the input data and the computational resources.
-Currently, ConfRover only supports protein conformation generation and models the coordinates of heavy atoms.
+ConfRover is trained on limited MD trajectories data and may not generalize well
+to out-of-distribution data.
+The quality of generated conformations is also limited by the quality of the input
+data and the computational resources.
+Currently, ConfRover only supports protein conformation generation and models the
+coordinates of heavy atoms.
 
 
 
@@ -166,4 +107,4 @@ Currently, ConfRover only supports protein conformation generation and models th
   journal={arXiv preprint arXiv:2505.17478},
   year={2025}
 }
-```
+```