Upload TFBertForSequenceClassification

README.md

@@ -1,100 +1,48 @@
 ---
 license: cc-by-sa-4.0
-language:
-- de
-- en
-- es
-- da
-- pl
-- sv
-- nl
-metrics:
-- accuracy
-pipeline_tag: text-classification
 tags:
-- partypress
-- political science
-- parties
-- press releases
+- generated_from_keras_callback
+model-index:
+- name: partypress-multilingual
+  results: []
 ---
 
-*currently the model only works on German texts*
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
 
-# PARTYPRESS multilingual
+# partypress-multilingual
 
-Fine-tuned model in seven languages on texts from nine countries, based on [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased). It used in Erfort et al. (2023).
+This model is a fine-tuned version of [cornelius/partypress-multilingual](https://huggingface.co/cornelius/partypress-multilingual) on an unknown dataset.
+It achieves the following results on the evaluation set:
 
 
 ## Model description
 
-The PARTYPRESS multilingual model builds on [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) but has a supervised component. This means, it was fine-tuned using texts labeled by humans. The labels indicate 23 different political issue categories derived from the Comparative Agendas Project (CAP). 
-
-
-## Model variations
-
-We plan to release monolingual models for each of the languages covered by this multilingual model.
+More information needed
 
 ## Intended uses & limitations
 
-The main use of the model is for text classification of press releases from political parties. It may also be useful for other political texts.
-
-### How to use
-
-This model can be used directly with a pipeline for text classification:
-
-```python
->>> from transformers import pipeline
->>> partypress = pipeline("text-classification", model = "cornelius/partypress-multilingual", tokenizer = "cornelius/partypress-multilingual")
->>> partypress("We urgently need to fight climate change and reduce carbon emissions. This is what our party stands for.")
-
-```
-
-### Limitations and bias
-
-The model was trained with data from parties in nine countries. For use in other countries, the model may be further fine-tuned. Without further fine-tuning, the performance of the model may be lower.
+More information needed
 
-The model may have biased predictions. We discuss some biases by country, party, and over time in the release paper for the PARTYPRESS database.
+## Training and evaluation data
 
-## Training data
-
-The PARTYPRESS multilingual model was fine-tuned with 27,243 press releases in seven languages on texts from 68 European parties in nine countries. The press releases were labeled by two expert human coders per country.
-
-For the training data of the underlying model, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
+More information needed
 
 ## Training procedure
 
-### Preprocessing
-
-For the preprocessing, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
-
-### Pretraining
-
-For the pretraining, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
-
-### Fine-tuning
-
-
-## Evaluation results
-
-Fine-tuned on our downstream task, this model achieves the following results in a five-fold cross validation:
+### Training hyperparameters
 
-| Accuracy | Precision | Recall  | F1 score |
-|:--------:|:---------:|:-------:|:--------:|
-|    69.52 |   67.99   | 67.60   |   66.77  |
+The following hyperparameters were used during training:
+- optimizer: None
+- training_precision: float32
 
-### BibTeX entry and citation info
+### Training results
 
-```bibtex
-@article{erfort_partypress_2023,
-  author    = {Cornelius Erfort and
-               Lukas F. Stoetzer and
-               Heike Klüver},
-  title     = {The PARTYPRESS Database: A New Comparative Database of Parties’ Press Releases},
-  journal   = {Research and Politics},
-  volume    = {forthcoming},
-  year      = {2023},
-}
-```
 
 
+### Framework versions
 
+- Transformers 4.28.0
+- TensorFlow 2.12.0
+- Datasets 2.12.0
+- Tokenizers 0.13.3

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "bert-base-multilingual-cased",
+  "_name_or_path": "cornelius/partypress-multilingual",
   "architectures": [
     "BertForSequenceClassification"
   ],

tf_model.h5

@@ -0,0 +1,3 @@
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+size 711772524