README.md

metadata

model-index:
  - name: poltextlab/finetune-agent-prod
    results:
      - task:
          type: text-classification
        metrics:
          - name: Accuracy
            type: accuracy
            value: N/A
          - name: F1-Score
            type: f1
            value: 86%
tags:
  - text-classification
  - pytorch
metrics:
  - precision
  - recall
  - 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

finetune-agent-prod

How to use the model

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/finetune-agent-prod",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token="<your_hf_read_only_token>"
)

text = "<text_to_classify>"
pipe(text)

Classification Report

Overall Performance:

• Accuracy: N/A
• Macro Avg: Precision: 0.86, Recall: 0.86, F1-score: 0.86
• Weighted Avg: Precision: 0.86, Recall: 0.86, F1-score: 0.86

Per-Class Metrics:

Label	Precision	Recall	F1-score	Support
(0_0) Procedural	1	0.94	0.97	35
(0_1) Commemorative / one-minute speech	0.78	0.88	0.83	33
(1_1) Relevant	0.8	0.75	0.77	32

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.