Create Readme.md

last-checkpoint/Readme.md

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+---
+license: apache-2.0
+language:
+- fr
+- en
+metrics:
+- chrf
+- sacrebleu
+base_model:
+- google-t5/t5-small
+pipeline_tag: translation
+---
+
+---
+library_name: transformers
+license: apache-2.0
+base_model: t5-small
+tags:
+- generated_from_trainer
+- translation
+- t5
+- english-to-french
+model-index:
+- name: t5-fra-eng
+  results:
+    - task: translation
+      dataset: Custom English-French dataset https://huggingface.co/datasets/SOULAMA/eng-fra-dataset
+
+---
+<!-- 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. -->
+
+# t5-eng-fra
+
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on a French-English translation dataset.
+
+## Model description
+
+`t5-fra-eng` is a sequence-to-sequence model designed to translate French text into English. It was fine-tuned from `t5-small` using Hugging Face’s `Seq2SeqTrainer` on a custom French-English dataset. The model leverages T5’s transformer architecture and the text-to-text paradigm for translation tasks.
+
+## Intended uses & limitations
+
+**Intended uses:**
+- Automatic translation of English text into French.
+- Machine translation research and experimentation.
+
+**Limitations:**
+- The model may produce incorrect translations for idiomatic expressions or rare vocabulary.
+- Not suitable for legal, medical, or critical translations without human verification.
+- Performance depends on the quality and size of the fine-tuning dataset.
+
+## Training and evaluation data
+
+- Dataset: Custom French-English parallel sentences.
+- Split: 80% training,  20% test from dataset.
+- Split  80% training,  20% test  from trainset
+- Data preprocessing: Text normalized, tokenized using `t5-small` tokenizer, maximum input length = 128 tokens, maximum target length = 128 tokens.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+- learning_rate: 2e-5
+- per_device_train_batch_size: 16
+- per_device_eval_batch_size: 16
+- num_train_epochs: 3
+- weight_decay: 0.01
+- optimizer: AdamW (betas=(0.9, 0.999), epsilon=1e-8)
+- lr_scheduler_type: linear
+- mixed_precision_training: Native AMP
+- seed: 42
+- evaluation_strategy: epoch
+- save_total_limit: 3
+- predict_with_generate: True
+
+### Training results
+
+- 'eval_loss': 0.5946913957595825,
+- 'eval_bleu': 42.4875053753255,
+- 'eval_chrf': 61.82547115972855,}
+
+### Framework versions
+
+- Transformers 4.57.3
+- PyTorch 2.6.0+cu126
+- Datasets 3.6.0
+- Tokenizers 0.22.1