Summarization
Transformers
Safetensors
t5
text2text-generation
Generated from Trainer
finance
text-generation-inference
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("yatharth97/T5-base-10K-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("yatharth97/T5-base-10K-summarization", device_map="auto")Quick Links
T5-Base-10K-Summarization
This model is a fine-tuned version of Google's T5-Base model tailored for summarizing financial 10K report sections.
Model description
T5-Base-10K-Summarization is optimized to condense lengthy 10K reports into manageable summaries, enabling quick insights into financial data and trends.
Intended uses & limitations
Ideal for use by financial analysts and regulatory agencies needing rapid insights from 10K reports. It may not be suited for summarizing non-financial documents or informal texts.
Training and evaluation data
Trained on a diverse collection of 10K reports from various industries, annotated for summarization to ensure broad applicability and accuracy.
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
- Downloads last month
- 4,654
# 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/T5-base-10K-summarization")