Xlm Roberta Large English Cap Minor Platforms

🚀 xlm-roberta-large-english-cap-minor-platforms

An xlm-roberta-large model finetuned on English training data for text classification.

🚀 Quick Start

This is an xlm-roberta-large model that has been fine - tuned on English training data. The data is labeled with minor topic codes from the Comparative Agendas Project.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-english-cap-minor-platforms",
    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)

Advanced Usage

# 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.
from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-english-cap-minor-platforms",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    # In earlier versions, use use_auth_token instead
    use_auth_token="<your_hf_read_only_token>" 
)

text = "Your input text here"
pipe(text)

📚 Documentation

Model Performance

The model was evaluated on a test set of 8922 examples (20% of the available data).

• Accuracy: 0.39.
• Weighted Average F1 - score: 0.3

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.

If you encounter a RuntimeError when loading the model using the from_pretrained() method, adding ignore_mismatched_sizes=True should solve the issue.

📄 License

This model is released under the MIT license.

🔍 Additional Information

Tags

• zero - shot - classification
• text - classification
• pytorch

Metrics

• accuracy
• f1 - score

Gated Access

Our models are intended for academic use only. If you are not affiliated with an academic institution, please provide a rationale for using our models. Please allow us a few business days to manually review subscriptions.

Gated Fields