readme: ---
  language:
  - en
  pipeline_tag: text-classification
  widget:
  - text: "And it was great to see how our Chinese team very much aware of that and of shifting all the resourcing to really tap into these opportunities."
    example_title: "Examplary Transformation Sentence"
  - text: "But we will continue to recruit even after that because we expect that the volumes are going to continue to grow."
    example_title: "Examplary Non-Transformation Sentence"
  - text: "So and again, we'll be disclosing the current taxes that are there in Guyana, along with that revenue adjustment."
    example_title: "Examplary Non-Transformation Sentence" 
  ---
  
  # TransformationTransformer
  
  **TransformationTransformer** is a fine-tuned [distilroberta](https://huggingface.co/distilroberta-base) model. It is trained and evaluated on 10,000 manually annotated sentences gleaned from the Q&A-section of quarterly earnings conference calls. In particular, it was trained on sentences issued by firm executives to discriminate between setnences that allude to **business transformation** vis-à-vis those that discuss topics other than business transformations. More details about the training procedure can be found [below](#model-training).
  
  
  ## Background
  
  Context on the project. 
  
  
  ## Usage
  
  The model is intented to be used for sentence classification: It creates a contextual text representation from the input sentence and outputs a probability value. `LABEL_1` refers to a sentence that is predicted to contains transformation-related content (vice versa for `LABEL_0`). The query should consist of a single sentence.
  
  
  ## Usage (API)
  
  ```python
  import json
  import requests
  
  API_TOKEN = <TOKEN>
  
  headers = {"Authorization": f"Bearer {API_TOKEN}"}
  API_URL = "https://api-inference.huggingface.co/models/simonschoe/call2vec"
  
  def query(payload):
      data = json.dumps(payload)
      response = requests.request("POST", API_URL, headers=headers, data=data)
      return json.loads(response.content.decode("utf-8"))
  
  query({"inputs": "<insert-sentence-here>"})
  ```
  
  ## Usage (transformers)
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("simonschoe/TransformationTransformer")
  model = AutoModelForSequenceClassification.from_pretrained("simonschoe/TransformationTransformer")
  
  classifier = pipeline('text-classification',  model=model, tokenizer=tokenizer)
  classifier('<insert-sentence-here>')
  ```
  
  
  ## Model Training
  
  The model has been trained on text data stemming from earnings call transcripts. The data is restricted to a call's question-and-answer (Q&A) section and the remarks by firm executives. The data has been segmented into individual sentences using [`spacy`](https://spacy.io/).
  
  **Statistics of Training Data:**
  - Labeled sentences: 10,000
  - Data distribution: xxx
  - Inter-coder agreement: xxx
  
  The following code snippets presents the training pipeline:
  <link to script>