---
model-index:
- name: poltextlab/xlm-roberta-large-pooled-emotions9-v2
  results:
  - task:
      type: text-classification
    metrics:
    - name: Accuracy
      type: accuracy
      value: 72%
    - name: F1-Score
      type: f1
      value: 72%
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-emotions9-v2

An `xlm-roberta-large` model finetuned on sentence-level multilingual training data hand-annotated.

## Codebook
The model assigns the following labels:

- **0**: "Anger"
- **1**: "Fear"
- **2**: "Disgust"
- **3**: "Sadness"
- **4**: "Joy"
- **5**: "None of Them"
- **6**: "Enthusiasm"
- **7**: "Hope"
- **8**: "Pride"

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-emotions9-v2",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token="<your_hf_read_only_token>"
)

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)
```

### Gated access
Due to the gated access, you must pass the `token` parameter when loading the model. In earlier versions of the Transformers package, you may need to use the `use_auth_token` parameter instead.

# Classification Report

## Overall Performance:

* **Accuracy:** 72%
* **Macro Avg:** Precision: 0.72, Recall: 0.73, F1-score: 0.72
* **Weighted Avg:** Precision: 0.72, Recall: 0.72, F1-score: 0.72

## Per-Class Metrics:

| Label        |   Precision |   Recall |   F1-score |   Support |
|:-------------|------------:|---------:|-----------:|----------:|
| Anger        |        0.55 |     0.54 |       0.55 |      5439 |
| Fear         |        0.76 |     0.8  |       0.78 |      5432 |
| Disgust      |        0.95 |     0.92 |       0.94 |      5432 |
| Sadness      |        0.84 |     0.84 |       0.84 |      5425 |
| Joy          |        0.8  |     0.81 |       0.8  |      5152 |
| None of Them |        0.69 |     0.65 |       0.67 |     11158 |
| Enthusiasm   |        0.64 |     0.6  |       0.62 |      5432 |
| Hope         |        0.5  |     0.59 |       0.54 |      5439 |
| Pride        |        0.78 |     0.77 |       0.78 |      5432 |

## 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.