model documentation

README.md

@@ -1,39 +1,178 @@
+
 ---
 language:
 - da
+license: cc-by-sa-4.0
 tags:
 - bert
 - pytorch
 - sentiment
 - polarity
-license: cc-by-sa-4.0
-datasets:
-- Twitter Sentiment
-- Europarl Sentiment
 metrics:
 - f1
 widget:
 - text: Det er super godt
 ---
 
-# Danish BERT Tone for sentiment polarity detection
-
-The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. 
+# Model Card for Danish BERT
+ Danish BERT Tone for sentiment polarity detection
+ 
+ 
+ 
+# Model Details
+ 
+## Model Description
+ 
+The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. It has been finetuned on the pretrained Danish BERT model by BotXO.
+ 
+- **Developed by:** DaNLP
+- **Shared by [Optional]:** Hugging Face
+- **Model type:** Text Classification
+- **Language(s) (NLP):** Danish (da)
+- **License:** cc-by-sa-4.0
+- **Related Models:** More information needed
+  - **Parent Model:** BERT
+- **Resources for more information:** 
+  - [GitHub Repo](https://github.com/certainlyio/nordic_bert) 
+  -  [Associated Documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone)
+ 
+ 
+# Uses
+ 
+## Direct Use
+ 
+This model can be used for text classification
+ 
+ 
+## Downstream Use [Optional]
+ 
+ 
+More information needed.
+ 
+ 
+## Out-of-Scope Use
+ 
+The model should not be used to intentionally create hostile or alienating environments for people. 
+ 
+# Bias, Risks, and Limitations
+ 
+ 
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+ 
+ 
+## Recommendations
+ 
+ 
+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
+ 
+The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets.
+ 
+## Training Procedure
+ 
+### Preprocessing
+ 
 It has been finetuned on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO. 
-
-See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-tone) for more details. 
-
-
-Here is how to use the model:
+ 
+### Speeds, Sizes, Times
+More information needed.
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed.
+ 
+### Factors
+ 
+ 
+ 
+### Metrics
+ 
+F1
+ 
+## Results 
+ 
+More information needed.
+ 
+# Model Examination
+ 
+More information needed.
+ 
+# Environmental Impact
+ 
+ 
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+ 
+- **Hardware Type:** More information needed.
+- **Hours used:** More information needed.
+- **Cloud Provider:** More information needed.
+- **Compute Region:** More information needed.
+- **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
+ 
+**BibTeX:**
+ 
+More information needed.
+ 
+**APA:**
+ 
+More information needed.
+ 
+# Glossary [optional]
+ 
+More information needed.
+ 
+# More Information [optional]
+ 
+More information needed.
+ 
+# Model Card Authors [optional]
+ 
+DaNLP in collaboration with Ezi Ozoani and the Hugging Face team
+ 
+# Model Card Contact
+ 
+More information needed.
+ 
+# How to Get Started with the Model
+ 
+Use the code below to get started with the model.
+<details>
+<summary> Click to expand </summary>
 
 ```python
 from transformers import BertTokenizer, BertForSequenceClassification
-
+ 
 model = BertForSequenceClassification.from_pretrained("DaNLP/da-bert-tone-sentiment-polarity")
 tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-tone-sentiment-polarity")
 ```
-
-## Training data
-
-The data used for training come from the [Twitter Sentiment](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#twitsent) and [EuroParl sentiment 2](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#europarl-sentiment2) datasets.
+</details>
+