Summarization
Transformers
TensorFlow
English
bart
text2text-generation
generated_from_keras_callback
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("KostiuchenkoArtem/bart_large_multi_modified")
model = AutoModelForSeq2SeqLM.from_pretrained("KostiuchenkoArtem/bart_large_multi_modified")Quick Links
KostiuchenkoArtem/bart_large_multi_modified
This model is a fine-tuned version of facebook/bart-large-cnn on Multi-News dataset. It achieves the following results on the evaluation set:
- Train Loss: 1.8945
- Validation Loss: 2.1223
- Epoch: 1
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 2.2231 | 2.1476 | 0 |
| 1.8945 | 2.1223 | 1 |
Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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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="KostiuchenkoArtem/bart_large_multi_modified")