README.md

metadata

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: >-
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  an academic institution, please provide a rationale for using our models.
  Please allow us a few business days to manually review subscriptions.
extra_gated_fields:
  Name: text
  Country: country
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  Please specify your academic use case: 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

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, 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.

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.