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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. |