Summarization
Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("amagzari/old")
model = AutoModelForSeq2SeqLM.from_pretrained("amagzari/old")Quick Links
t5-v1_1-small-finetuned-cnn_dailymail
This model is a fine-tuned version of google/t5-v1_1-small on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 1.7290
- Rouge1: 0.3363
- Rouge2: 0.1736
- Rougel: 0.2951
- Rougelsum: 0.3151
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: 5.6e-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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 2.7338 | 1.0 | 35890 | 1.8390 | 0.3278 | 0.1658 | 0.2876 | 0.3064 |
| 2.3233 | 2.0 | 71780 | 1.7779 | 0.3335 | 0.1713 | 0.2924 | 0.3124 |
| 2.2253 | 3.0 | 107670 | 1.7428 | 0.3348 | 0.1728 | 0.2941 | 0.3138 |
| 2.1797 | 4.0 | 143560 | 1.7290 | 0.3363 | 0.1736 | 0.2951 | 0.3151 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Downloads last month
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Dataset used to train amagzari/old
Evaluation results
- Rouge1 on cnn_dailymailself-reported0.336
# 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="amagzari/old")