README.md

---
model-index:
- name: poltextlab/xlm-roberta-large-i5-binary-codebook-v14
  results:
  - task:
      type: text-classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: N/A
    - name: F1-Score
      type: f1
      value: 76%
tags:
- text-classification
- pytorch
metrics:
- precision
- recall
- f1-score
language:
- en
base_model:
- xlm-roberta-large
pipeline_tag: text-classification
library_name: transformers
license: cc-by-4.0
extra_gated_prompt: Our models are intended for academic projects and academic research
  only.If you are not affiliated with an academic institution, please reach out to
  us at huggingface [at] poltextlab [dot] com for further inquiry.If we cannot clearly
  determine your academic affiliation and use case based on your form data, your request
  may be rejected. Please allow us a few business days to manually review subscriptions.
extra_gated_fields:
  Name: text
  Country: country
  Institution: text
  Institution Email: text
  Please specify your academic use case: text
---

# xlm-roberta-large-i5-binary-codebook-v14


# How to use the model

```python
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-i5-binary-codebook-v14",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token="<your_hf_read_only_token>"
)

text = "<text_to_classify>"
pipe(text)
```
        

# Classification Report

## Overall Performance:

* **Accuracy:** N/A
* **Macro Avg:** Precision: 0.76, Recall: 0.76, F1-score: 0.76
* **Weighted Avg:** Precision: 0.76, Recall: 0.76, F1-score: 0.76

## Per-Class Metrics:

| Label             |   Precision |   Recall |   F1-score |   Support |
|:------------------|------------:|---------:|-----------:|----------:|
| (0) Not illiberal |        0.79 |     0.77 |       0.78 |        30 |
| (1) Illiberal     |        0.73 |     0.76 |       0.75 |        25 |

# Inference platform
This model is used by the [CAP Babel Machine](https://babel.poltextlab.com), an open-source and free natural language processing tool, designed to simplify and speed up projects for comparative research.  

# Cooperation
Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP-coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [CAP Babel Machine](https://babel.poltextlab.com).
## Debugging and issues
This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually.