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--- |
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model-index: |
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- name: poltextlab/media2-25-26-v1-1001 |
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results: |
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- task: |
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type: text-classification |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 71% |
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- name: F1-Score |
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type: f1 |
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value: 70% |
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tags: |
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- text-classification |
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- transformers |
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- roberta |
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metrics: |
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- accuracy |
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- f1_score |
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language: |
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- en |
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base_model: |
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- xlm-roberta-large |
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pipeline_tag: text-classification |
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library_name: transformers |
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license: cc-by-4.0 |
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extra_gated_prompt: Our models are intended for academic use only. If you are not |
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affiliated with an academic institution, please provide a rationale for using our |
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models. Please allow us a few business days to manually review subscriptions. |
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extra_gated_fields: |
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Name: text |
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Country: country |
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Institution: text |
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Institution Email: text |
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Please specify your academic use case: text |
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--- |
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# media2-25-26-v1-1001 |
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This model uses the poltextLAB Media2 codebook built on top of the CAP codebook. |
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# How to use the model |
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```python |
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from transformers import AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large") |
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pipe = pipeline( |
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model="poltextlab/media2-25-26-v1-1001", |
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task="text-classification", |
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tokenizer=tokenizer, |
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use_fast=False, |
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token="<your_hf_read_only_token>" |
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) |
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text = "<text_to_classify>" |
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pipe(text) |
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``` |
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# Classification Report |
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## Overall Performance: |
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Evaluated on a test set of 1601 English samples. |
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* **Accuracy:** 71% |
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* **Macro Avg:** Precision: 0.67, Recall: 0.62, F1-score: 0.62 |
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* **Weighted Avg:** Precision: 0.74, Recall: 0.71, F1-score: 0.70 |
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## Per-Class Metrics: |
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| Label | Precision | Recall | F1-score | Support | |
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|--------:|------------:|---------:|-----------:|----------:| |
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| 1 | 0.77 | 0.8 | 0.78 | 50 | |
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| 2 | 0.74 | 0.78 | 0.76 | 50 | |
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| 3 | 0.74 | 0.74 | 0.74 | 50 | |
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| 4 | 0.7 | 0.86 | 0.77 | 50 | |
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| 5 | 0.86 | 0.76 | 0.81 | 50 | |
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| 6 | 0.83 | 0.98 | 0.9 | 50 | |
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| 7 | 0.85 | 0.88 | 0.86 | 50 | |
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| 8 | 0.87 | 0.94 | 0.9 | 50 | |
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| 9 | 0.87 | 0.82 | 0.85 | 50 | |
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| 10 | 0.77 | 0.94 | 0.85 | 50 | |
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| 12 | 0.56 | 0.88 | 0.69 | 50 | |
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| 13 | 0.88 | 0.86 | 0.87 | 50 | |
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| 14 | 0.73 | 0.76 | 0.75 | 50 | |
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| 15 | 0.51 | 0.86 | 0.64 | 50 | |
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| 16 | 0.75 | 0.86 | 0.8 | 50 | |
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| 17 | 0.63 | 0.76 | 0.69 | 50 | |
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| 18 | 0.91 | 0.82 | 0.86 | 50 | |
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| 19 | 0.51 | 0.82 | 0.63 | 50 | |
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| 20 | 0.62 | 0.92 | 0.74 | 50 | |
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| 21 | 0.75 | 0.8 | 0.78 | 50 | |
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| 23 | 0.52 | 0.78 | 0.62 | 50 | |
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| 24 | 0.71 | 0.57 | 0.63 | 42 | |
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| 25 | 0.92 | 0.48 | 0.63 | 23 | |
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| 26 | 0.92 | 0.56 | 0.7 | 43 | |
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| 27 | 0 | 0 | 0 | 18 | |
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| 28 | 0 | 0 | 0 | 9 | |
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| 29 | 0.43 | 0.27 | 0.33 | 33 | |
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| 30 | 0.72 | 0.28 | 0.41 | 46 | |
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| 31 | 0.89 | 0.44 | 0.59 | 36 | |
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| 32 | 0 | 0 | 0 | 20 | |
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| 33 | 0.12 | 0.08 | 0.1 | 12 | |
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| 34 | 0.07 | 0.14 | 0.1 | 7 | |
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| 35 | 0.93 | 0.71 | 0.81 | 35 | |
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| 36 | 0 | 0 | 0 | 3 | |
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| 37 | 1 | 0.82 | 0.9 | 44 | |
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| 38 | 0.81 | 0.81 | 0.81 | 42 | |
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| 39 | 1 | 0.39 | 0.57 | 33 | |
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| 40 | 0.88 | 0.21 | 0.34 | 33 | |
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| 41 | 1 | 0.78 | 0.88 | 32 | |
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| 998 | 0.92 | 0.55 | 0.69 | 40 | |
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# Inference platform |
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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. |
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# Cooperation |
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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). |
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## Debugging and issues |
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This architecture uses the `sentencepiece` tokenizer. In order to run the model before `transformers==4.27` you need to install it manually. |
