model documentation

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README.md

@@ -1,168 +1,39 @@
 ---
 language:
 - da
-license: apache-2.0
+tags:
+- bert
+- pytorch
+- sentiment
+- polarity
+license: cc-by-sa-4.0
+datasets:
+- Twitter Sentiment
+- Europarl Sentiment
+metrics:
+- f1
 widget:
 - text: Det er super godt
 ---
 
-# 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
- 
+# Danish BERT Tone for sentiment polarity detection
+
+The BERT Tone model detects sentiment polarity (positive, neutral or negative) in Danish texts. 
 It has been finetuned on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO. 
- 
-### 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>
+
+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:
 
 ```python
 from transformers import BertTokenizer, BertForSequenceClassification
- 
-model = BertForSequenceClassification.from_pretrained("alexandrainst/da-sentiment-base")
-tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-sentiment-base")
+
+model = BertForSequenceClassification.from_pretrained("DaNLP/da-bert-tone-sentiment-polarity")
+tokenizer = BertTokenizer.from_pretrained("DaNLP/da-bert-tone-sentiment-polarity")
 ```
-</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.
+

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