
# 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>
{"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"}]}
transformers pytorch safetensors roberta text-classification en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us 


# TransformationTransformer

TransformationTransformer is a fine-tuned distilroberta 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.


## 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)



## Usage (transformers)




## 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'.

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>
# TransformationTransformer

TransformationTransformer is a fine-tuned distilroberta 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. ## 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) ## Usage (transformers) ## 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'.

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>
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us 
 # TransformationTransformer

TransformationTransformer is a fine-tuned distilroberta 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. ## 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) ## Usage (transformers) ## 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'.

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>
passage: TAGS
#transformers #pytorch #safetensors #roberta #text-classification #en #autotrain_compatible #endpoints_compatible #region-us 
# TransformationTransformer

TransformationTransformer is a fine-tuned distilroberta 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.## 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)## Usage (transformers)## 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'.

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>