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--- |
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library_name: transformers |
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base_model: danasone/bart-small-ru-en |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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model-index: |
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- name: bart_hin_eng_mt |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bart_hin_eng_mt |
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This model is a fine-tuned version of [danasone/bart-small-ru-en](https://huggingface.co/danasone/bart-small-ru-en) on [cfilt/iitb-english-hindi](https://huggingface.co/datasets/cfilt/iitb-english-hindi) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9000 |
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- Bleu: 12.0235 |
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- Gen Len: 33.4107 |
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## Model description |
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Machine Translation model from Hindi to English on bart small model. |
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## Inference and evaluation |
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```python |
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import torch |
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import evaluate |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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class BartSmall(): |
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def __init__(self, model_path = 'ar5entum/bart_hin_eng_mt', device = None): |
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self.tokenizer = AutoTokenizer.from_pretrained(model_path) |
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self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path) |
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if not device: |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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self.device = device |
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self.model.to(device) |
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def predict(self, input_text): |
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inputs = self.tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True).to(self.device) |
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pred_ids = self.model.generate(inputs.input_ids, max_length=512, num_beams=4, early_stopping=True) |
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prediction = self.tokenizer.decode(pred_ids[0], skip_special_tokens=True) |
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return prediction |
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def predict_batch(self, input_texts, batch_size=32): |
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all_predictions = [] |
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for i in range(0, len(input_texts), batch_size): |
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batch_texts = input_texts[i:i+batch_size] |
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inputs = self.tokenizer(batch_texts, return_tensors="pt", max_length=512, |
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truncation=True, padding=True).to(self.device) |
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with torch.no_grad(): |
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pred_ids = self.model.generate(inputs.input_ids, |
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max_length=512, |
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num_beams=4, |
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early_stopping=True) |
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predictions = self.tokenizer.batch_decode(pred_ids, skip_special_tokens=True) |
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all_predictions.extend(predictions) |
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return all_predictions |
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model = BartSmall(device='cuda') |
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input_texts = [ |
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"यह शोध्य रकम है।", |
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"जानने के लिए देखें ये वीडियो.", |
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"वह दो बेटियों व एक बेटे का पिता था।" |
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] |
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ground_truths = [ |
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"This is a repayable amount.", |
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"Watch this video to find out.", |
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"He was a father of two daughters and a son." |
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] |
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import time |
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start = time.time() |
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predictions = model.predict_batch(input_texts, batch_size=len(input_texts)) |
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end = time.time() |
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print("TIME: ", end-start) |
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for i in range(len(input_texts)): |
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print("‾‾‾‾‾‾‾‾‾‾‾‾") |
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print("Input text:\t", input_texts[i]) |
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print("Prediction:\t", predictions[i]) |
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print("Ground Truth:\t", ground_truths[i]) |
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bleu = evaluate.load("bleu") |
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results = bleu.compute(predictions=predictions, references=ground_truths) |
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print(results) |
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# TIME: 1.2374696731567383 |
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# ‾‾‾‾‾‾‾‾‾‾‾‾ |
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# Input text: यह शोध्य रकम है। |
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# Prediction: This is a repayable amount. |
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# Ground Truth: This is a repayable amount. |
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# ‾‾‾‾‾‾‾‾‾‾‾‾ |
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# Input text: जानने के लिए देखें ये वीडियो. |
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# Prediction: View these videos to know. |
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# Ground Truth: Watch this video to find out. |
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# ‾‾‾‾‾‾‾‾‾‾‾‾ |
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# Input text: वह दो बेटियों व एक बेटे का पिता था। |
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# Prediction: He was a father of two daughters and a son. |
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# Ground Truth: He was a father of two daughters and a son. |
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# {'bleu': 0.747875245486914, 'precisions': [0.8260869565217391, 0.75, 0.7647058823529411, 0.7857142857142857], 'brevity_penalty': 0.9574533680683809, 'length_ratio': 0.9583333333333334, 'translation_length': 23, 'reference_length': 24} |
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``` |
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## Training Procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 100 |
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- eval_batch_size: 40 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 200 |
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- total_eval_batch_size: 80 |
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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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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 15.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| |
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| 2.6298 | 1.0 | 8265 | 2.6192 | 4.5435 | 39.8786 | |
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| 2.2656 | 2.0 | 16530 | 2.2836 | 8.2498 | 35.8339 | |
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| 2.0625 | 3.0 | 24795 | 2.1747 | 9.9182 | 35.5214 | |
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| 1.974 | 4.0 | 33060 | 2.0760 | 10.1515 | 33.9732 | |
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| 1.925 | 5.0 | 41325 | 2.0285 | 10.7702 | 34.175 | |
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| 1.8076 | 6.0 | 49590 | 1.9860 | 11.4286 | 34.8875 | |
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| 1.7817 | 7.0 | 57855 | 1.9664 | 11.4579 | 32.6411 | |
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| 1.7025 | 8.0 | 66120 | 1.9561 | 11.9226 | 33.5179 | |
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| 1.6691 | 9.0 | 74385 | 1.9354 | 11.7352 | 33.2161 | |
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| 1.6631 | 10.0 | 82650 | 1.9231 | 11.9303 | 32.7679 | |
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| 1.6317 | 11.0 | 90915 | 1.9264 | 11.5889 | 32.625 | |
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| 1.6449 | 12.0 | 99180 | 1.9047 | 11.8451 | 33.8554 | |
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| 1.6165 | 13.0 | 107445 | 1.9040 | 12.0755 | 32.7661 | |
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| 1.5826 | 14.0 | 115710 | 1.9000 | 12.3137 | 33.3536 | |
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| 1.5835 | 15.0 | 123975 | 1.9000 | 12.0235 | 33.4107 | |
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### Framework versions |
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- Transformers 4.45.0.dev0 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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