AutonLab
/

MOMENT-1-large

@@ -28,7 +28,7 @@ For details on MOMENT models, training data, and experimental results, please re
 Install the package using:
 ```bash
-pip install git+https://github.com/moment-timeseries-foundation-model/moment-test.git
 ```
 To load the pre-trained model for one of the tasks, use one of the following code snippets:
@@ -100,110 +100,6 @@ model = MOMENTPipeline.from_pretrained(
 - **Paper:** https://arxiv.org/abs/2402.03885
 - **Demo:** https://github.com/moment-timeseries-foundation-model/
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
 ## Environmental Impact
@@ -218,23 +114,11 @@ We use the Total Graphics Power (TGP) to calculate the total power consumed for
 - **Compute Region:** Pittsburgh, USA
 - **Carbon Emission (tCO2eq):**
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
 #### Hardware
 All models were trained and evaluated on a computing cluster consisting of 128 AMD EPYC 7502 CPUs, 503 GB of RAM, and 8 NVIDIA RTX A6000 GPUs each with 49 GiB RAM. All MOMENT variants were trained on a single A6000 GPU (with any data or model parallelism).
-#### Software
-## Citation [optional]
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
@@ -242,34 +126,15 @@ All models were trained and evaluated on a computing cluster consisting of 128 A
 If you use MOMENT please cite our paper:
 ```bibtex
-    @article{
-        goswami2024moment,
-        title={{MOMENT: A Family of Open Time-series Foundation Models}},
-        author={Goswami, Mononito and Szafer, Konrad and Choudhry, Arjun and Cai, Yifu and Li, Shuo and Dubrawski, Artur},
-        journal={arXiv preprint arXiv:2402.03885},
-        year={2024},
-    }
 ```
 **APA:**
 Goswami, M., Szafer, K., Choudhry, A., Cai, Y., Li, S., & Dubrawski, A. (2024).
 MOMENT: A Family of Open Time-series Foundation Models. arXiv preprint arXiv:2402.03885.
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact

 Install the package using:
 ```bash
+pip install git+https://github.com/moment-timeseries-foundation-model/moment.git
 ```
 To load the pre-trained model for one of the tasks, use one of the following code snippets:
 - **Paper:** https://arxiv.org/abs/2402.03885
 - **Demo:** https://github.com/moment-timeseries-foundation-model/
 ## Environmental Impact
 - **Compute Region:** Pittsburgh, USA
 - **Carbon Emission (tCO2eq):**
 #### Hardware
 All models were trained and evaluated on a computing cluster consisting of 128 AMD EPYC 7502 CPUs, 503 GB of RAM, and 8 NVIDIA RTX A6000 GPUs each with 49 GiB RAM. All MOMENT variants were trained on a single A6000 GPU (with any data or model parallelism).
+## Citation
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 If you use MOMENT please cite our paper:
 ```bibtex
+@inproceedings{goswami2024moment,
+  title={MOMENT: A Family of Open Time-series Foundation Models},
+  author={Mononito Goswami and Konrad Szafer and Arjun Choudhry and Yifu Cai and Shuo Li and Artur Dubrawski},
+  booktitle={ICML},
+  year={2024}
+}
 ```
 **APA:**
 Goswami, M., Szafer, K., Choudhry, A., Cai, Y., Li, S., & Dubrawski, A. (2024).
 MOMENT: A Family of Open Time-series Foundation Models. arXiv preprint arXiv:2402.03885.