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ybracke
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transnormer-19c-beta-v02

Text2Text Generation
Transformers
PyTorch
Safetensors
German
t5
Generated from Trainer
Eval Results
text-generation-inference
Inference Endpoints
Model card Files Community
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  1. README.md +85 -0
  2. config.json +32 -0
  3. generation_config.json +7 -0
  4. pytorch_model.bin +3 -0
  5. special_tokens_map.json +150 -0
  6. tokenizer_config.json +157 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/byt5-small
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+ language: de
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+ model-index:
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+ - name: ybracke/transnormer-19c-beta-v02
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+ results:
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+ - task:
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+ name: Historic Text Normalization
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+ type: translation
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+ dataset:
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+ name: DTA EvalCorpus
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+ type: N/A
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+ split: test
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+ metrics:
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+ - name: Word Accuracy
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+ type: accuracy
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+ value: 0.98878
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+ - name: Word Accuracy (case insensitive)
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+ type: accuracy
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+ value: 0.99343
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+ ---
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+
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+
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+
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+ # Transnormer 19th century (beta v01)
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+
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+ This model normalizes spelling variants in historical German text to the modern spelling.
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+ We fine-tuned [google/byt5-small](https://huggingface.co/google/byt5-small) on a modified version of the [DTA EvalCorpus](https://kaskade.dwds.de/~moocow/software/dtaec/) (1780-1901).
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+
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+ ## Model description
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+
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+ ### Demo Usage
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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+ from transformers.generation import GenerationConfig
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+ tokenizer = AutoTokenizer.from_pretrained("ybracke/transnormer-19c-beta-v02")
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+ model = AutoModelForSeq2SeqLM.from_pretrained("ybracke/transnormer-19c-beta-v02")
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+ gen_cfg = GenerationConfig.from_model_config(model.config)
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+ gen_cfg.max_new_tokens = 512
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+ sentence = ""
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+ inputs = tokenizer(sentence, return_tensors="pt",)
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+ outputs = model.generate(**inputs, generation_config=gen_cfg)
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+ print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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+ # >>> ['']
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+ ```
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+
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+ Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html):
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+
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+ ```python
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+ from transformers import pipeline
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+ transnormer = pipeline('text2text-generation', model='ybracke/transnormer-19c-beta-v02')
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+ sentence = ""
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+ print(transnormer(sentence))
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+ # >>> [{'generated_text': ''}]
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+ ```
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 10
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.13.3
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+ {
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+ "_name_or_path": "google/byt5-small",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 3584,
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+ "d_kv": 64,
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+ "d_model": 1472,
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+ "decoder_start_token_id": 0,
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+ "dense_act_fn": "gelu_new",
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gated-gelu",
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+ "gradient_checkpointing": false,
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "is_gated_act": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "num_decoder_layers": 4,
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+ "num_heads": 6,
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+ "num_layers": 12,
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+ "pad_token_id": 0,
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "tie_word_embeddings": false,
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+ "tokenizer_class": "ByT5Tokenizer",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.31.0",
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+ "use_cache": true,
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+ "vocab_size": 384
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+ }
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