readme: ---
  license: apache-2.0
  datasets:
  - MtCelesteMa/multiglue
  language:
  - en
  metrics:
  - accuracy
  pipeline_tag: text-classification
  ---
  
  # Model Card for Model ID
  
  <!-- Provide a quick summary of what the model is/does. -->
  
  This model is bert-base-uncased finetuned on the MultiGLUE dataset.
  
  # 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):** English
  - **License:** Apache 2.0 (same as BERT)
  - **Finetuned from model [optional]:** bert-base-uncased
  
  ## 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.
  
  ```python
  import numpy as np
  import transformers
  
  tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-uncased')
  model = transformers.AutoModelForSequenceClassification.from_pretrained('MtCelesteMa/bert-base-uncased-finetuned-multiglue')
  
  task = 'cola'
  sentence1 = 'Our friends won\'t buy this analysis, let alone the next one we propose.'
  sentence2 = None
  
  inputs = tokenizer(f'{task}:{sentence1}', f'{sentence2}', return_tensors='pt')
  outputs = model(**inputs)
  label = np.argmax(outputs.logits[0].detach().numpy())
  print(label)
  ```
  
  # 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 [optional]
  
  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
  
  ### Preprocessing
  
  [More Information Needed]
  
  ### Speeds, Sizes, Times
  
  <!-- 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:** RTX A6000
  - **Hours used:** 2
  - **Cloud Provider:** [vast.ai](https://vast.ai)
  - **Compute Region:** Sweden
  - **Carbon Emitted:** 0.26 kg
  
  # 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, inclu