Update README.md

README.md

@@ -2,198 +2,71 @@
 datasets:
 - code_search_net
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Architecture
+
+This model follows the distilroberta-base architecture. Futhermore, this model was initialized with the checkpoint of distilroberta-base.
+
+# Pre-training phase
+
+This model was pre-trained with the MLM objective (`mlm_probability=0.15`).
+
+During this phase, the inputs had the following format:
+$$\left[[CLS], t_1, \dots, t_n, [SEP], w_1, \dots, w_m\right[EOS]]$$
+where $t_1, \dots, t_n$ are the code tokens and $w_1, \dots, w_m$ are the natural language description tokens. More concretely, this is the snippet that tokenizes the input:
+```python
+def tokenize_function_bimodal(examples, tokenizer, max_len):
+    codes = [' '.join(example) for example in examples['func_code_tokens']]
+    nls = [' '.join(example) for example in examples['func_documentation_tokens']]
+    pairs = [[c, nl] for c, nl in zip(codes, nls)]
+    return tokenizer(pairs, max_length=max_len, padding="max_length", truncation=True)
+```
+
+# Training details
+
+- Max length: 512
+- Effective batch size: 64
+- Total steps: 60000
+- Learning rate: 5e-4
+
+# Usage
+
+```python
+model = AutoModelForMaskedLM.from_pretrained('antolin/distilroberta-base-csn-python-bimodal')
+tokenizer = AutoTokenizer.from_pretrained('antolin/distilroberta-base-csn-python-bimodal')
+mask_filler = pipeline("fill-mask", model=model, tokenizer=tokenizer)
+code_tokens = ["def", "<mask>", "(", "a", ",", "b", ")", ":", "if", "a", ">", "b", ":", "return", "a", "else", "return", "b"]
+nl_tokens = ["return", "the", "maximum", "value"]
+input_text = ' '.join(code_tokens) + tokenizer.sep_token + ' '.join(nl_tokens)
+pprint(mask_filler(input_text, top_k=5))
+```
+```shell
+[{'score': 0.4645618796348572,
+  'sequence': 'def max ( a, b ) : if a > b : return a else return b  return '
+              'the maximum value',
+  'token': 19220,
+  'token_str': ' max'},
+ {'score': 0.40963634848594666,
+  'sequence': 'def maximum ( a, b ) : if a > b : return a else return b  '
+              'return the maximum value',
+  'token': 4532,
+  'token_str': ' maximum'},
+ {'score': 0.02103462442755699,
+  'sequence': 'def min ( a, b ) : if a > b : return a else return b  return '
+              'the maximum value',
+  'token': 5251,
+  'token_str': ' min'},
+ {'score': 0.014217409305274487,
+  'sequence': 'def value ( a, b ) : if a > b : return a else return b  return '
+              'the maximum value',
+  'token': 923,
+  'token_str': ' value'},
+ {'score': 0.010762304067611694,
+  'sequence': 'def minimum ( a, b ) : if a > b : return a else return b  '
+              'return the maximum value',
+  'token': 3527,
+  'token_str': ' minimum'}]
+```
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [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]
-
-## 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 Data 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 Data 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
-
-<!-- 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]