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--- |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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widget: |
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- 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." |
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example_title: "Examplary Transformation Sentence" |
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- text: "But we will continue to recruit even after that because we expect that the volumes are going to continue to grow." |
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example_title: "Examplary Non-Transformation Sentence" |
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- text: "So and again, we'll be disclosing the current taxes that are there in Guyana, along with that revenue adjustment." |
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example_title: "Examplary Non-Transformation Sentence" |
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--- |
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# TransformationTransformer |
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**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). |
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## Background |
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Context on the project. |
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## Usage |
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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. |
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## Usage (API) |
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```python |
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import json |
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import requests |
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API_TOKEN = <TOKEN> |
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headers = {"Authorization": f"Bearer {API_TOKEN}"} |
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API_URL = "https://api-inference.huggingface.co/models/simonschoe/call2vec" |
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def query(payload): |
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data = json.dumps(payload) |
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response = requests.request("POST", API_URL, headers=headers, data=data) |
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return json.loads(response.content.decode("utf-8")) |
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query({"inputs": "<insert-sentence-here>"}) |
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``` |
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## Usage (transformers) |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("simonschoe/TransformationTransformer") |
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model = AutoModelForSequenceClassification.from_pretrained("simonschoe/TransformationTransformer") |
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classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) |
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classifier('<insert-sentence-here>') |
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``` |
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## Model Training |
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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/). |
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**Statistics of Training Data:** |
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- Labeled sentences: 10,000 |
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- Data distribution: xxx |
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- Inter-coder agreement: xxx |
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The following code snippets presents the training pipeline: |
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<link to script> |
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