End of training

README.md

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+---
+license: apache-2.0
+base_model: google/mt5-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+- wer
+model-index:
+- name: nep-spell-mt5-small-0
+  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. -->
+
+# nep-spell-mt5-small-0
+
+This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6065
+- Accuracy: 0.0
+- Precision: 0.0
+- Recall: 0.0
+- F1: 0.0
+- Wer: 7.0137
+- Cer: 13.4164
+- Chrf: 1.5380
+- Exact Match: 0.0
+- Bertscore:precision: 0.4936
+- Bertscore:recall: 0.5422
+- Bertscore:f1: 0.5139
+- Ter: 701.3722
+- Blerurt: -0.5287
+
+## 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: 5e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1  | Wer     | Cer     | Chrf   | Exact Match | Bertscore:precision | Bertscore:recall | Bertscore:f1 | Ter       | Blerurt |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---:|:-------:|:-------:|:------:|:-----------:|:-------------------:|:----------------:|:------------:|:---------:|:-------:|
+| 39.0737       | 0.39  | 500  | 14.0796         | 0.0      | 0.0       | 0.0    | 0.0 | 28.9335 | 58.5317 | 0.3292 | 0.0         | 0.3772              | 0.5413           | 0.4436       | 2893.3486 | -0.9012 |
+| 4.5775        | 0.79  | 1000 | 1.6065          | 0.0      | 0.0       | 0.0    | 0.0 | 7.0137  | 13.4164 | 1.5380 | 0.0         | 0.4936              | 0.5422           | 0.5139       | 701.3722  | -0.5287 |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "google/mt5-small",
+  "architectures": [
+    "MT5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 1024,
+  "d_kv": 64,
+  "d_model": 512,
+  "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,
+  "model_type": "mt5",
+  "num_decoder_layers": 8,
+  "num_heads": 6,
+  "num_layers": 8,
+  "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": "4.37.2",
+  "use_cache": true,
+  "vocab_size": 250112
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "decoder_start_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.37.2"
+}

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