Update README.md

1st edit on model card

README.md

@@ -1,11 +1,15 @@
 ---
 library_name: transformers
-tags: []
+license: gpl-3.0
+datasets:
+- phunc20/nj_biergarten_captcha_v2
+base_model:
+- microsoft/trocr-base-handwritten
 ---
 
-# Model Card for Model ID
+# Model Card for trocr-base-handwritten_nj_biergarten_captcha_v2
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a model for CAPTCHA OCR.
 
 
 
@@ -13,53 +17,26 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **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]
+This is a simple model finetuned from `microsoft/trocr-base-handwritten` on a dataset
+I created at `phunc20/nj_biergarten_captcha_v2`.
 
 ## 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]
+Although the model seems to perform well on the dataset `phunc20/nj_biergarten_captcha_v2`,
+it does not exhibit such good performance across all CAPTCHA images. In this respect, this
+model is worse than Human.
 
 ### Recommendations
 
@@ -77,40 +54,26 @@ Use the code below to get started with the model.
 
 ### 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]
+Like I mentioned, I trained this model on `phunc20/nj_biergarten_captcha_v2`.
+In particular, I trained on the `train` split and evalaute on `validation` split,
+without touching the `test` split.
 
 ### 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]
+Please refer to
+<https://gitlab.com/phunc20/captchew/-/blob/main/colab_notebooks/train_from_pretrained_Seq2SeqTrainer_torchDataset.ipynb?ref_type=heads>
+which is adapted from
+<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TrOCR/Fine_tune_TrOCR_on_IAM_Handwriting_Database_using_Seq2SeqTrainer.ipynb>
 
 ## 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]
+1. The `test` split of `phunc20/nj_biergarten_captcha_v2`
+2. This Kaggle dataset <https://www.kaggle.com/datasets/fournierp/captcha-version-2-images/data>
+   (we shall call this dataset by the name of `kaggle_test_set` in this model card.)
 
 #### Factors
 
@@ -120,80 +83,33 @@ Use the code below to get started with the model.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+CER, exact match and average length difference. The former two can be found in HuggingFace's
+documentation. The last one is just one metric I care a little about. It is quite easy to
+understand and, if need be, explanation could be found at the source code:
+<https://gitlab.com/phunc20/captchew/-/blob/v0.1/average_length_difference.py>
 
 ### Results
+On the `test` split of `phunc20/nj_biergarten_captcha_v2`
 
-[More Information Needed]
-
-#### Summary
+|                            Model                          |   cer    | exact match | avg len diff |
+| --------------------------------------------------------- | -------- | ----------- | ------------ |
+| `phunc20/trocr-base-handwritten_nj_biergarten_captcha_v2` | 0.001333 |   496/500   |     1/500    |
+| `microsoft/trocr-base-handwritten`                        |   0.9    |     5/500   |      2.4     |
 
+On `kaggle_test_set`
 
+|                            Model                          |   cer    | exact match | avg len diff |
+| --------------------------------------------------------- | -------- | ----------- | ------------ |
+| `phunc20/trocr-base-handwritten_nj_biergarten_captcha_v2` |  0.4381  |   69/1070   |    0.1289    |
+| `microsoft/trocr-base-handwritten`                        |  1.0112  |   17/1070   |    2.4439    |
 
-## 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:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## 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
-
-[More Information Needed]
+- **Carbon Emitted:** [More Information Needed]