| --- |
| model-index: |
| - name: poltextlab/media2-25-26-v1-1001 |
| results: |
| - task: |
| type: text-classification |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 71% |
| - name: F1-Score |
| type: f1 |
| value: 70% |
| tags: |
| - text-classification |
| - transformers |
| - roberta |
| metrics: |
| - accuracy |
| - 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: |
| 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 |
| --- |
| |
| # media2-25-26-v1-1001 |
|
|
| This model uses the poltextLAB Media2 codebook built on top of the CAP codebook. |
|
|
|
|
| # How to use the model |
|
|
| ```python |
| from transformers import AutoTokenizer, pipeline |
| |
| tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large") |
| pipe = pipeline( |
| model="poltextlab/media2-25-26-v1-1001", |
| task="text-classification", |
| tokenizer=tokenizer, |
| use_fast=False, |
| token="<your_hf_read_only_token>" |
| ) |
| |
| text = "<text_to_classify>" |
| pipe(text) |
| ``` |
| |
| |
| # Classification Report |
|
|
| ## Overall Performance: |
|
|
| Evaluated on a test set of 1601 English samples. |
|
|
| * **Accuracy:** 71% |
| * **Macro Avg:** Precision: 0.67, Recall: 0.62, F1-score: 0.62 |
| * **Weighted Avg:** Precision: 0.74, Recall: 0.71, F1-score: 0.70 |
|
|
| ## Per-Class Metrics: |
|
|
| | Label | Precision | Recall | F1-score | Support | |
| |--------:|------------:|---------:|-----------:|----------:| |
| | 1 | 0.77 | 0.8 | 0.78 | 50 | |
| | 2 | 0.74 | 0.78 | 0.76 | 50 | |
| | 3 | 0.74 | 0.74 | 0.74 | 50 | |
| | 4 | 0.7 | 0.86 | 0.77 | 50 | |
| | 5 | 0.86 | 0.76 | 0.81 | 50 | |
| | 6 | 0.83 | 0.98 | 0.9 | 50 | |
| | 7 | 0.85 | 0.88 | 0.86 | 50 | |
| | 8 | 0.87 | 0.94 | 0.9 | 50 | |
| | 9 | 0.87 | 0.82 | 0.85 | 50 | |
| | 10 | 0.77 | 0.94 | 0.85 | 50 | |
| | 12 | 0.56 | 0.88 | 0.69 | 50 | |
| | 13 | 0.88 | 0.86 | 0.87 | 50 | |
| | 14 | 0.73 | 0.76 | 0.75 | 50 | |
| | 15 | 0.51 | 0.86 | 0.64 | 50 | |
| | 16 | 0.75 | 0.86 | 0.8 | 50 | |
| | 17 | 0.63 | 0.76 | 0.69 | 50 | |
| | 18 | 0.91 | 0.82 | 0.86 | 50 | |
| | 19 | 0.51 | 0.82 | 0.63 | 50 | |
| | 20 | 0.62 | 0.92 | 0.74 | 50 | |
| | 21 | 0.75 | 0.8 | 0.78 | 50 | |
| | 23 | 0.52 | 0.78 | 0.62 | 50 | |
| | 24 | 0.71 | 0.57 | 0.63 | 42 | |
| | 25 | 0.92 | 0.48 | 0.63 | 23 | |
| | 26 | 0.92 | 0.56 | 0.7 | 43 | |
| | 27 | 0 | 0 | 0 | 18 | |
| | 28 | 0 | 0 | 0 | 9 | |
| | 29 | 0.43 | 0.27 | 0.33 | 33 | |
| | 30 | 0.72 | 0.28 | 0.41 | 46 | |
| | 31 | 0.89 | 0.44 | 0.59 | 36 | |
| | 32 | 0 | 0 | 0 | 20 | |
| | 33 | 0.12 | 0.08 | 0.1 | 12 | |
| | 34 | 0.07 | 0.14 | 0.1 | 7 | |
| | 35 | 0.93 | 0.71 | 0.81 | 35 | |
| | 36 | 0 | 0 | 0 | 3 | |
| | 37 | 1 | 0.82 | 0.9 | 44 | |
| | 38 | 0.81 | 0.81 | 0.81 | 42 | |
| | 39 | 1 | 0.39 | 0.57 | 33 | |
| | 40 | 0.88 | 0.21 | 0.34 | 33 | |
| | 41 | 1 | 0.78 | 0.88 | 32 | |
| | 998 | 0.92 | 0.55 | 0.69 | 40 | |
|
|
| # 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. |
