Update README.md

README.md

@@ -1,7 +1,5 @@
 ---
 license: mit
-tags:
-- generated_from_trainer
 metrics:
 - accuracy
 - precision
@@ -31,9 +29,43 @@ This model was trained on 115,943 manually annotated sentences to classify text
 
 ## Intended uses & limitations
 
-More information needed
+The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance.
+
+```python
+from transformers import pipeline
+import pandas as pd
+classifier = pipeline(
+    task="text-classification",
+    model="niksmer/ManiBERT")
+# Load text data you want to classify
+text = pd.read_csv("text.csv")
+# Inference
+output = classifier(text)
+# Print output
+pd.DataFrame(output).head()
+```
+
+## Train Data
+
+ManiBERT was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020. 
+
+
+| Country        | Count manifestos | Count sentences | Time span        |
+|----------------|------------------|-----------------|--------------------|
+| Australia      | 18               | 14,887          | 2010-2016          |
+| Ireland        | 23               | 24,966          | 2007-2016          |
+| Canada         | 14               | 12,344          | 2004-2008 & 2015   |
+| New Zealand    | 46               | 35,079          | 1993-2017          |
+| South Africa   | 29               | 13,334          | 1994-2019          |
+| USA            | 9                | 13,188          | 1992   & 2004-2020 |
+| United Kingdom | 34               | 30,936          | 1997-2019          |
+
+Canadian manifestos between 2004 and 2008 are used as test data.
+
+The resulting Datasets are higly (!) imbalanced. See Evaluation.
+
+## Evaluation
 
-## Training and evaluation data
 
 | Description                                                       | Label | Count Train Data | Count Validation Data | Count Test Data | Validation F1-Score | Test F1-Score |
 |-------------------------------------------------------------------|-------|------------------|-----------------------|-----------------|---------------------|---------------|
@@ -99,15 +131,24 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 64
-- eval_batch_size: 64
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.05
-- num_epochs: 5
-- mixed_precision_training: Native AMP
+```
+training_args = TrainingArguments(
+    warmup_ratio=0.05,
+    weight_decay=0.1, 
+    learning_rate=5e-05,
+    fp16 = True,
+    evaluation_strategy="epoch",
+    num_train_epochs=5,
+    per_device_train_batch_size=16,
+    overwrite_output_dir=True,
+    per_device_eval_batch_size=16,
+    save_strategy="no",
+    logging_dir='logs',   
+    logging_strategy= 'steps',     
+    logging_steps=10,
+    push_to_hub=True,
+    hub_strategy="end")
+```
 
 ### Training results
 
@@ -119,6 +160,23 @@ The following hyperparameters were used during training:
 | 0.9263        | 4.0   | 7248 | 1.5173          | 0.5975   | 0.5975   | 0.4499   | 0.5901      | 0.5975    | 0.5975 |
 | 0.7859        | 5.0   | 9060 | 1.5574          | 0.5978   | 0.5978   | 0.4564   | 0.5903      | 0.5978    | 0.5978 |
 
+### Overall evaluation
+
+| Type | Micro F1-Score | Macro F1-Score | Weighted F1-Score |
+|----------------|----------------|----------------|-------------------|
+| Validation |  0.60           | 0.46           | 0.59             |
+| Test |  0.48           | 0.30           | 0.47             |
+
+### Evaluation based on saliency theory
+
+Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent.
+Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology.
+
+The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions).
+
+In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE).
+
+![image](english_manibert_manifesto.png)
 
 ### Framework versions