---
model-index:
- name: poltextlab/xlm-roberta-large-pooled-emotions10-v2
  results:
  - task:
      type: text-classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: 81%
    - name: F1-Score
      type: f1
      value: 81%
tags:
- text-classification
- pytorch
metrics:
- precision
- recall
- f1-score
language:
- en
- hu
- fr
- cs
- sk
- pl
- de
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:
  Country: country
  Institution: text
  Institution Email: text
  Full Name: text
  Please specify your academic project/use case you want to use the models for: text
---

# xlm-roberta-large-pooled-emotions10-v2

An `xlm-roberta-large` model finetuned on sentence-level multilingual training data hand-annotated using the following labels:

- **0**: "Neutral"
- **1**: "Anger"
- **2**: "Fear"
- **3**: "Disgust"
- **4**: "Sadness"
- **5**: "Joy"
- **6**: "Hope"
- **7**: "Enthusiasm"
- **8**: "Pride"
- **9**: "Other emotion"

The training data we used was augmented translated texts. It covers 7 languages (English, German, French, Polish, Slovak, Czech and Hungarian) with nearly identical shares.

## 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-pooled-emotions10-v2",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
)

text = "We will place an immediate 6-month halt on the finance driven closure of beds and wards, and set up an independent audit of needs and facilities."
pipe(text)
```

# Classification Report

## Overall Performance:

* **Accuracy:** 81%  
* **Macro Avg:** Precision: 0.82, Recall: 0.81, F1-score: 0.81  
* **Weighted Avg:** Precision: 0.81, Recall: 0.81, F1-score: 0.81  

## Per-Class Metrics:

| Label            | Precision | Recall | F1-score | Support |
|:-----------------|----------:|-------:|---------:|--------:|
| Neutral (0)      | 0.81 | 0.88 | 0.85 | 9367 |
| Anger (1)        | 0.73 | 0.70 | 0.72 | 5433 |
| Fear (2)         | 0.86 | 0.84 | 0.85 | 5434 |
| Disgust (3)      | 0.95 | 0.95 | 0.95 | 5437 |
| Sadness (4)      | 0.90 | 0.85 | 0.88 | 5434 |
| Joy (5)          | 0.84 | 0.85 | 0.85 | 5162 |
| Hope (6)         | 0.59 | 0.63 | 0.61 | 5437 |
| Enthusiasm (7)   | 0.70 | 0.63 | 0.67 | 5433 |
| Pride (8)        | 0.82 | 0.82 | 0.82 | 5435 |
| Other emotion (9)| 0.98 | 0.95 | 0.97 | 2051 |

Total samples: **54,623**

## Inference platform
This model is used by the [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 coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the [Babel Machine](https://babel.poltextlab.com).

## Debugging and issues
This architecture uses the `sentencepiece` tokenizer. In order to use the model before `transformers==4.27` you need to install it manually.

If you encounter a `RuntimeError` when loading the model using the `from_pretrained()` method, adding `ignore_mismatched_sizes=True` should solve the issue.