Nhoseinnia
/

MT5base_en2fa_translation

@@ -14,63 +14,16 @@ pipeline_tag: text2text-generation
 using Mt5-Base for english to persian translation
-## Model Details
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
 This model is designed to automatically translate English text to Farsi, which uses the pre-trained model of MT5, which is a multilingual seq2seq model.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
 - **Model type:** MT5-base.
 - **Language(s) (NLP):** english to persian.
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## 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
@@ -86,30 +39,12 @@ Use the code below to get started with the model.
 this model train with 36.000 text en 2 fa from persiannlp/parsinlu_translation_en_fa dataSet
-### 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
 - **Number of Epochs:** 2 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 - **Training Batch Size:** 8
 - **evaluation Batch Size:** 8
-#### 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
@@ -119,88 +54,3 @@ this model train with 36.000 text en 2 fa from persiannlp/parsinlu_translation_e
 this model test with 4.000 text en 2 fa from persiannlp/parsinlu_translation_en_fa dataSet
-#### 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
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## 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. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 using Mt5-Base for english to persian translation
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
 This model is designed to automatically translate English text to Farsi, which uses the pre-trained model of MT5, which is a multilingual seq2seq model.
 - **Model type:** MT5-base.
 - **Language(s) (NLP):** english to persian.
 ## How to Get Started with the Model
 this model train with 36.000 text en 2 fa from persiannlp/parsinlu_translation_en_fa dataSet
 #### Training Hyperparameters
 - **Number of Epochs:** 2 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 - **Training Batch Size:** 8
 - **evaluation Batch Size:** 8
 ### Testing Data, Factors & Metrics
 this model test with 4.000 text en 2 fa from persiannlp/parsinlu_translation_en_fa dataSet