Update README.md

README.md

@@ -1,11 +1,13 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
 ---
 
-# Model Card for Model ID
+# Model Card for EncT5
 
-<!-- Provide a quick summary of what the model is/does. -->
+EncT5 is a variant of T5 that utilizes mainly the encoder for non-autoregressive (ie. classification and regression)
+tasks. The model is from the paper [Fine-tuning T5 Encoder for Non-autoregressive Tasks](https://arxiv.org/abs/2110.08426)
+by Frederick Liu, Terry Huang, Shihang Lyu, Siamak Shakeri, Hongkun Yu, Jing Li
 
 
 
@@ -13,189 +15,44 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+EncT5 uses the same base weights at T5, but **must be fine-tuning before use**. There are several special features
+to EncT5:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+1. There are less decoder layers (a single decoder layer by default), and so has fewer parameters/resources than the
+   standard T5.
+3. There is a separate decoder word embedding, with the decoder input ids being predefined constants. During
+   fine-tuning, these constants are trained to effectively "prompt" the encoder to perform the necessary
+   classification/regression tasks.
+4. There is a classification head on top of the decoder output.
 
-- **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]
+Research has shown that this model can be more efficient and usable over T5 and BERT for non-autoregressive
+tasks such as classification and regression.
 
-### 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.
+- **Developed by:** Frederick Liu, Terry Huang, Shihang Lyu, Siamak Shakeri, Hongkun Yu, Jing Li. See
+  [associated paper](https://arxiv.org/abs/2110.08426)
+- **Model type:** Language Model
+- **Language(s) (NLP):** English, French, Romanian, German
+- **License:** Apache 2.0
+- **Based on model:** [T5](https://huggingface.co/google-t5/t5-base)
+- **Repository:** [Github repro](https://github.com/hackyon/EncT5)
+- **Paper:** [Fine-tuning T5 Encoder for Non-autoregressive Tasks](https://arxiv.org/abs/2110.08426)
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+  model = AutoModelForSequenceClassification.from_pretrained("hackyon/enct5-base", trust_remote_code=True)
+  # Fine-tune the model before use.
+
+See the [github repro](https://github.com/hackyon/EncT5) for a more comprehensive guide.
 
 ## 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]
+The weights of this model are directly copied from [t5-base](https://huggingface.co/google-t5/t5-base).
 
 ### 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
-
-<!-- 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]
-
+This model **must be fine-tuned** before use. The decoder word embedding and classification head are both untrained.