Update README.md

README.md

@@ -4,13 +4,70 @@ language:
 pipeline_tag: text-classification
 tags:
 widget:
-- text: "transformation"
-  example_title: "transformation"
-- text: "sustainability"
-  example_title: "sustainability"
+- 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" 
   
 ---
 
-# Model Card
+# TransformationTransformer
 
-Description here
+**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>