Summarization
Transformers
PyTorch
TensorBoard
English
bart
text2text-generation
Generated from Trainer
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Quake24/easyTermsSummerizer")
model = AutoModelForSeq2SeqLM.from_pretrained("Quake24/easyTermsSummerizer")Quick Links
easyTermsSummerizer
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8124
- Rouge1: 0.7533
- Rouge2: 0.6964
- Rougel: 0.6806
- Rougelsum: 0.6793
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 2 | 2.2083 | 0.7332 | 0.6595 | 0.6374 | 0.6376 |
| No log | 2.0 | 4 | 1.9331 | 0.7776 | 0.7268 | 0.6991 | 0.7005 |
| No log | 3.0 | 6 | 1.8124 | 0.7533 | 0.6964 | 0.6806 | 0.6793 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
- Downloads last month
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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="Quake24/easyTermsSummerizer")