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README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags: []
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- 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. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+
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+
config.json ADDED
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+ {
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+ "_name_or_path": "/project/lt200056-opgpth/knot/myscript/Multimodal/Continue_Training_CLIP/Experiment/contrastive/outputs/pretrained_caption_model",
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+ "architectures": [
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+ "CLIPTextCamembertModelWithProjection"
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+ ],
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+ "attention_dropout": 0.1,
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+ "attention_probs_dropout_prob": 0.1,
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+ "auto_map": {
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+ "AutoConfig": "configuration_clip_camembert.CLIPTextCamembertConfig",
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+ "AutoModel": "modeling_clip_camembert.CLIPTextCamembertModelWithProjection"
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+ },
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "initializer_factor": 1.0,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "clip_text_camembert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "projection_dim": 512,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.37.0",
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+ "type_vocab_size": 1,
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+ "use_cache": true,
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+ "vocab_size": 25005
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+ }
configuration_clip_camembert.py ADDED
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+ from transformers import CamembertConfig
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+
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+
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+ class CLIPTextCamembertConfig(CamembertConfig):
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+ # ref : https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased/blob/main/config.json
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+ model_type = "clip_text_camembert"
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+
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+ def __init__(
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+ self,
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+ vocab_size=25005,
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+ hidden_size=768,
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+ intermediate_size=3072,
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+ projection_dim=512,
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+ num_hidden_layers=12,
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+ num_attention_heads=12,
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+ max_position_embeddings=512,
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+ hidden_act="gelu",
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+ layer_norm_eps=1e-12,
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+ attention_dropout=0.1,
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+ initializer_range=0.02,
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+ initializer_factor=1.0,
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+ pad_token_id=1,
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+ bos_token_id=0,
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+ eos_token_id=2,
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+ type_vocab_size=1,
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+ **kwargs,
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+ ):
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+ super().__init__(
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+ pad_token_id=pad_token_id,
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+ bos_token_id=bos_token_id,
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+ eos_token_id=eos_token_id,
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+ **kwargs,
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+ )
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+
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+ self.vocab_size = vocab_size
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+ self.hidden_size = hidden_size
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+ self.intermediate_size = intermediate_size
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+ self.projection_dim = projection_dim
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+ self.num_hidden_layers = num_hidden_layers
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+ self.num_attention_heads = num_attention_heads
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+ self.max_position_embeddings = max_position_embeddings
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+ self.layer_norm_eps = layer_norm_eps
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+ self.hidden_act = hidden_act
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+ self.initializer_range = initializer_range
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+ self.initializer_factor = initializer_factor
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+ self.attention_dropout = attention_dropout
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+ self.type_vocab_size = type_vocab_size
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+ self.auto_map = {
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+ "AutoConfig": "configuration_clip_camembert.CLIPTextCamembertConfig",
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+ "AutoModel": "modeling_clip_camembert.CLIPTextCamembertModelWithProjection",
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+ }
model.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:827e46c81eff27861cd95815354c6b5b62585133e93b2761258d9d6db81d0f97
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+ size 422575160
modeling_clip_camembert.py ADDED
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+ from .configuration_clip_camembert import CLIPTextCamembertConfig
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+ from transformers import (
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+ CamembertModel,
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+ CLIPTextModelWithProjection,
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+ )
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+ from transformers.models.clip.modeling_clip import CLIPTextModelOutput
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+ import torch
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+ from torch import nn
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+ from typing import Any, Optional, Tuple, Union
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+
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+
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+ class CLIPTextCamembertModelWithProjection(CLIPTextModelWithProjection):
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+ config_class = CLIPTextCamembertConfig
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+
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+ def __init__(self, config: CLIPTextCamembertConfig):
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+ super().__init__(config)
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+
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+ self.text_model = CamembertModel(config)
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+
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+ self.text_projection = nn.Linear(
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+ config.hidden_size, config.projection_dim, bias=False
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+ )
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+ # Initialize weights and apply final processing
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+ self.post_init()
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+
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+ def forward(
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+ self,
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+ input_ids: Optional[torch.Tensor] = None,
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+ attention_mask: Optional[torch.Tensor] = None,
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+ position_ids: Optional[torch.Tensor] = None,
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+ output_attentions: Optional[bool] = None,
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+ output_hidden_states: Optional[bool] = None,
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+ return_dict: Optional[bool] = None,
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+ ) -> Union[Tuple, CLIPTextModelOutput]:
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+ return_dict = (
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+ return_dict if return_dict is not None else self.config.use_return_dict
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+ )
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+
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+ text_outputs = self.text_model(
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+ input_ids=input_ids,
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+ attention_mask=attention_mask,
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+ position_ids=position_ids,
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+ output_attentions=output_attentions,
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+ output_hidden_states=output_hidden_states,
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+ return_dict=return_dict,
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+ )
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+
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+ pooled_output = text_outputs[1]
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+
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+ text_embeds = self.text_projection(pooled_output)
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+
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+ if not return_dict:
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+ outputs = (text_embeds, text_outputs[0]) + text_outputs[2:]
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+ return tuple(output for output in outputs if output is not None)
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+
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+ return CLIPTextModelOutput(
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+ text_embeds=text_embeds,
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+ last_hidden_state=text_outputs.last_hidden_state,
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+ hidden_states=text_outputs.hidden_states,
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+ attentions=text_outputs.attentions,
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+ )
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+
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+ def converter_weight(
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+ self, path_model="airesearch/wangchanberta-base-att-spm-uncased"
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+ ):
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+ r"""
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+ converter weight from airesearch/wangchanberta-base-att-spm-uncased
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+ """
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+ pretrained_state_dict = CamembertModel.from_pretrained(path_model).state_dict()
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+ # Load the new state dictionary into the custom model
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+ self.text_model.load_state_dict(pretrained_state_dict)