Update README.md

README.md

@@ -73,106 +73,29 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-Please refer to <https://huggingface.co/spaces/liujch1998/vera/blob/main/app.py#L27-L98>
-
-## 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. -->
-
-See the **Data Construction** section of our paper.
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-See the **Model Training** section of our paper.
-
-#### 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. -->
-
-See the **Evaluation Results** section of our paper.
-
-### 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:** 
-- **Hours used:** 
-- **Cloud Provider:** 
-- **Compute Region:** 
-- **Carbon Emitted:** 
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
+```python
+tokenizer = transformers.AutoTokenizer.from_pretrained('liujch1998/vera')
+model = transformers.T5EncoderModel.from_pretrained('liujch1998/vera')
+model.D = model.shared.embedding_dim
+linear = torch.nn.Linear(model.D, 1, dtype=model.dtype)
+linear.weight = torch.nn.Parameter(model.shared.weight[32099, :].unsqueeze(0))
+linear.bias = torch.nn.Parameter(model.shared.weight[32098, 0].unsqueeze(0))
+model.eval()
+t = model.shared.weight[32097, 0].item() # temperature for calibration
+
+statement = 'Please enter a commonsense statement here.'
+input_ids = tokenizer.batch_encode_plus([statement], return_tensors='pt', padding='longest', truncation='longest_first', max_length=128).input_ids
+with torch.no_grad():
+    output = model(input_ids)
+    last_hidden_state = output.last_hidden_state
+    hidden = last_hidden_state[0, -1, :]
+    logit = linear(hidden).squeeze(-1)
+    logit_calibrated = logit / t
+    score_calibrated = logit_calibrated.sigmoid()
+    # score_calibrated is Vera's final output plausibility score
+```
+
+You may also refer to <https://huggingface.co/spaces/liujch1998/vera/blob/main/app.py#L27-L98> for implementation.
 
 ## Citation [optional]
 
@@ -180,25 +103,15 @@ See the **Evaluation Results** section of our paper.
 
 **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]
+```
+@article{Liu2023VeraAG,
+  title={Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statements},
+  author={Jiacheng Liu and Wenya Wang and Dianzhuo Wang and Noah A. Smith and Yejin Choi and Hanna Hajishirzi},
+  journal={ArXiv},
+  year={2023},
+  volume={abs/2305.03695}
+}
+```
 
 ## Model Card Contact