# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("yatharth97/BART-10K-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("yatharth97/BART-10K-summarization")BART-10K-Summarization
This model is a fine-tuned version of Facebook's BART model specifically for summarizing financial 10K report sections.
Model description
BART-10K-Summarization is designed to produce concise summaries of detailed financial reports, assisting analysts and stakeholders in quickly understanding key information without needing to parse the entire document.
Intended uses & limitations
This model is intended to aid financial analysts, investors, and regulatory bodies by summarizing sections of 10K reports. It may not perform well on non-financial texts or highly technical documents outside the scope of standard financial reporting.
Training and evaluation data
The model was trained on a curated dataset of 10K financial reports, each annotated with executive summaries by experienced financial analysts.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1
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# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="yatharth97/BART-10K-summarization")