readme: ---
  language: en
  license: apache-2.0
  datasets:
  - sst2
  - glue
  model-index:
  - name: distilbert-base-uncased-finetuned-sst-2-english
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: glue
        type: glue
        config: sst2
        split: validation
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9105504587155964
        verified: true
      - name: Precision
        type: precision
        value: 0.8978260869565218
        verified: true
      - name: Recall
        type: recall
        value: 0.9301801801801802
        verified: true
      - name: AUC
        type: auc
        value: 0.9716626673402374
        verified: true
      - name: F1
        type: f1
        value: 0.9137168141592922
        verified: true
      - name: loss
        type: loss
        value: 0.39013850688934326
        verified: true
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: sst2
        type: sst2
        config: default
        split: train
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9885521685548412
        verified: true
      - name: Precision Macro
        type: precision
        value: 0.9881965062029833
        verified: true
      - name: Precision Micro
        type: precision
        value: 0.9885521685548412
        verified: true
      - name: Precision Weighted
        type: precision
        value: 0.9885639626373408
        verified: true
      - name: Recall Macro
        type: recall
        value: 0.9886145346602994
        verified: true
      - name: Recall Micro
        type: recall
        value: 0.9885521685548412
        verified: true
      - name: Recall Weighted
        type: recall
        value: 0.9885521685548412
        verified: true
      - name: F1 Macro
        type: f1
        value: 0.9884019815052447
        verified: true
      - name: F1 Micro
        type: f1
        value: 0.9885521685548412
        verified: true
      - name: F1 Weighted
        type: f1
        value: 0.9885546181087554
        verified: true
      - name: loss
        type: loss
        value: 0.040652573108673096
        verified: true
  ---
  
  # DistilBERT base uncased finetuned SST-2
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
  - [Uses](#uses)
  - [Risks, Limitations and Biases](#risks-limitations-and-biases)
  - [Training](#training)
  
  ## Model Details
  **Model Description:** This model is a fine-tune checkpoint of [DistilBERT-base-uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned on SST-2.
  This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7).
  - **Developed by:** Hugging Face
  - **Model Type:** Text Classification
  - **Language(s):** English
  - **License:** Apache-2.0
  - **Parent Model:** For more details about DistilBERT, we encourage users to check out [this model card](https://huggingface.co/distilbert-base-uncased).
  - **Resources for more information:**
      - [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification)
  
  ## How to Get Started With the Model
  
  Example of single-label classification:
  ​​
  ```python
  import torch
  from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
  
  tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
  model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
  
  inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
  with torch.no_grad():
      logits = model(**inputs).logits
  
  predicted_class_id = logits.argmax().item()
  model.config.id2label[predicted_class_id]
  
  ```
  
  ## Uses
  
  #### Direct Use
  
  This model can be used for  topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
  
  #### Misuse and Out-of-scope Use
  The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
  
  
  ## Risks, Limitations and Biases
  
  Based on a few experimentations, we observed that this model could produce biased predictions that target underrepresented populations.
  
  For instance, for sentences like `This film was filmed in COUNTRY`, this binary classification model will give radically different probabilities for the positive label depending on the country (0.89 if the country is France, but 0.08 if the country is Afghanistan) when nothing in the input indicates such a strong semantic shift. In this [colab](https://colab.research.google.com/gist/ageron/fb2f64fb145b4bc7c49efc97e5f114d3/biasmap.ipynb), [Aurélien Géron](https:/