| | --- |
| | language: |
| | - en |
| | pipeline_tag: text-classification |
| | tags: |
| | 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> |
| |
|
