Update README.md

README.md

@@ -3,97 +3,43 @@ language:
 - 'da'
 - 'no'
 library_name: transformers
+f1-score: 0.76
 ---
-# Model Card for A-og-ttack2
+# Model Card for A&ttack2
 
 Text classification model that determines whether a not a short text contains an attack.
 
-# Model Details
 
-## Model Description
+# Model Description
 
-The model is based on the T5 architecture, and is trained on a large Danish and Norwegian corpus of text. The model is further fine-tuned on ~70.000 
+The model is based on the [North-T5-NCC Large](https://huggingface.co/north/t5_large_NCC) (developed by Per E. Kummervold) which is a Scandinavian language built upon [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x). The model is further trained on ~70k Norwegian and ~67k Danish social media posts which have been classified as either 'attack' or 'not attack', making it a text-to-text model manipulated to do classification. The model is described in Danish in [this report](https://strapi.ogtal.dk/uploads/966f1ebcfa9942d3aef338e9920611f4.pdf).
 
 
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Developed by:** The development team at Analyse & Tal
+- **Model type:** Language model restricted to classification
+- **Language(s) (NLP):** Danish and Norwegian
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model:** [More information needed] 
 
-## Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+# Direct Use
+The model can be used directly to classify Danish and Norwegian social media posts (or similar pieces of text).
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-# Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-## Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-## Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
 
 # Bias, Risks, and Limitations
-
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-## Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-# Training Details
-
-## Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-## Training Procedure [optional]
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-### Preprocessing
-
 [More Information Needed]
 
-### Speeds, Sizes, Times
+# Training Data
+A collection of ~70k Norwegian and ~67k Danish social media posts have been manually annotated as 'attack' or 'not attack' by six individual coders. 5% of the posts have been annotated by more then one annotator, with the annotators in agreement for 83% of annotations.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+*Hvad er data-split metoden? Hvad er training-validation-test split?*
 
 # Evaluation
-
 <!-- This section describes the evaluation protocols and provides the results. -->
-
 ## Testing Data, Factors & Metrics
 
 ### Testing Data
-
 <!-- This should link to a Data Card if possible. -->
 
 [More Information Needed]
@@ -106,7 +52,7 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Macro-averaged f1-score: 0.76
 
 [More Information Needed]
 
@@ -118,11 +64,6 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 
 
-# Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 # Environmental Impact
 
@@ -132,65 +73,33 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 - **Hardware Type:** [More Information Needed]
 - **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
+- **Cloud Provider:** Azure
+- **Compute Region:** North-Europe
 - **Carbon Emitted:** [More Information Needed]
 
-# Technical Specifications [optional]
-
-## Model Architecture and Objective
-
-[More Information Needed]
-
-## Compute Infrastructure
-
-[More Information Needed]
-
-### Hardware
-
-[More Information Needed]
-
-### Software
-
-[More Information Needed]
-
-# Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
 
-**APA:**
+# Model Card Authors
+This model card was written by the developer team at Analyse & Tal. Contact: oyvind@ogtal.dk.
 
-[More Information Needed]
-
-# Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-# More Information [optional]
-
-[More Information Needed]
-
-# Model Card Authors [optional]
-
-[More Information Needed]
+# How to Get Started with the Model
 
-# Model Card Contact
+Use the code below to get started with the model.
 
-[More Information Needed]
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# How to Get Started with the Model
+# Download/load tokenizer and language model
+tokenizer = AutoTokenizer.from_pretrained("ogtal/A-og-ttack2")
+model = AutoModelForSeq2SeqLM.from_pretrained("ogtal/A-og-ttack2")
 
-Use the code below to get started with the model.
+# Give sample text. The example is from a social media comment.
+sample_text = "Velbekomme dit klamme usle løgnersvin!"
+input_ids = tokenizer("Velbekomme", return_tensors="pt").input_ids
 
-<details>
-<summary> Click to expand </summary>
+# Forward pass and print the output
+outputs = model.generate(input_ids)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-[More Information Needed]
+Running the above code will print "angreb" (attack in Danish)
 
-</details>