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_v50
+  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_v50
+
+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: 1.8296
+- Rouge1: 0.5063
+- Rouge2: 0.2803
+- Rougel: 0.4415
+- Rougelsum: 0.4405
+- Bert precision: 0.8741
+- Bert recall: 0.8787
+- Average word count: 8.7857
+- Max word count: 16
+- Min word count: 3
+- Average token count: 16.3942
+- % shortened texts with length > 12: 11.9048
+
+## 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: 0.0001
+- 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: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
+| 1.3343        | 1.0   | 83   | 1.4625          | 0.5101 | 0.2866 | 0.4536 | 0.4527    | 0.8751         | 0.877       | 8.3042             | 19             | 4              | 15.1508             | 5.0265                             |
+| 0.7011        | 2.0   | 166  | 1.4296          | 0.5101 | 0.284  | 0.4548 | 0.4551    | 0.8736         | 0.8797      | 8.7593             | 18             | 5              | 16.0529             | 7.672                              |
+| 0.483         | 3.0   | 249  | 1.3880          | 0.5025 | 0.2819 | 0.4433 | 0.442     | 0.8722         | 0.8782      | 8.7698             | 18             | 5              | 14.8492             | 6.3492                             |
+| 0.3876        | 4.0   | 332  | 1.7614          | 0.4934 | 0.2653 | 0.4334 | 0.4327    | 0.8715         | 0.8725      | 8.2249             | 18             | 5              | 16.3042             | 5.5556                             |
+| 0.291         | 5.0   | 415  | 1.8296          | 0.5063 | 0.2803 | 0.4415 | 0.4405    | 0.8741         | 0.8787      | 8.7857             | 16             | 3              | 16.3942             | 11.9048                            |
+
+
+### Framework versions
+
+- Transformers 4.33.1
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "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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