End of training

README.md

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+---
+license: mit
+base_model: facebook/bart-large-xsum
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: text_shortening_model_v60
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# text_shortening_model_v60
+
+This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7251
+- Rouge1: 0.7246
+- Rouge2: 0.5572
+- Rougel: 0.6745
+- Rougelsum: 0.6724
+- Bert precision: 0.9227
+- Bert recall: 0.9242
+- Bert f1-score: 0.923
+- Average word count: 8.4018
+- Max word count: 16
+- Min word count: 4
+- Average token count: 16.1562
+- % shortened texts with length > 12: 7.5893
+
+## 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: 1e-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
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:-------------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
+| 1.4241        | 1.0   | 49   | 0.7533          | 0.7094 | 0.5458 | 0.6655 | 0.6641    | 0.9182         | 0.9214      | 0.9193        | 8.3884             | 17             | 5              | 15.3661             | 6.25                               |
+| 0.5792        | 2.0   | 98   | 0.7279          | 0.7058 | 0.5397 | 0.6587 | 0.6582    | 0.9201         | 0.9193      | 0.9192        | 8.3393             | 17             | 4              | 15.9062             | 5.3571                             |
+| 0.4392        | 3.0   | 147  | 0.7251          | 0.7246 | 0.5572 | 0.6745 | 0.6724    | 0.9227         | 0.9242      | 0.923         | 8.4018             | 16             | 4              | 16.1562             | 7.5893                             |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

generation_config.json

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+{
+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "early_stopping": true,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "max_length": 62,
+  "min_length": 11,
+  "no_repeat_ngram_size": 3,
+  "num_beams": 6,
+  "pad_token_id": 1,
+  "transformers_version": "4.33.1"
+}

pytorch_model.bin

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