Training in progress, step 1000

README.md

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+---
+license: apache-2.0
+base_model: Danish-summarisation/DanSumT5-large
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+model-index:
+- name: DanSumT5-large-finetuned-test_57626
+  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. -->
+
+# DanSumT5-large-finetuned-test_57626
+
+This model is a fine-tuned version of [Danish-summarisation/DanSumT5-large](https://huggingface.co/Danish-summarisation/DanSumT5-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.5370
+- Rouge1: 31.992
+- Rouge2: 7.6605
+- Rougel: 18.1676
+- Rougelsum: 29.1825
+- Gen Len: 126.27
+
+## 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: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
+| No log        | 1.0   | 200  | 2.5665          | 31.8442 | 7.5263 | 18.0111 | 29.1725   | 126.86  |
+| No log        | 2.0   | 400  | 2.5370          | 31.992  | 7.6605 | 18.1676 | 29.1825   | 126.27  |
+
+
+### Framework versions
+
+- Transformers 4.32.1
+- Pytorch 2.1.0
+- Datasets 2.12.0
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "Danish-summarisation/DanSumT5-large",
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2816,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "length_penalty": 5.0,
+  "max_length": 128,
+  "min_length": 9,
+  "model_type": "mt5",
+  "no_repeat_ngram_size": 3,
+  "num_beams": 2,
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.32.1",
+  "use_cache": true,
+  "vocab_size": 250112
+}

generation_config.json

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+{
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "length_penalty": 5.0,
+  "max_length": 128,
+  "min_length": 9,
+  "no_repeat_ngram_size": 3,
+  "num_beams": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.32.1"
+}

pytorch_model.bin

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special_tokens_map.json

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+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

spiece.model

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tokenizer_config.json

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+  "clean_up_tokenization_spaces": true,
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+  "legacy": true,
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+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "tokenizer_file": null,
+  "unk_token": "<unk>"
+}

training_args.bin

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