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--- |
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library_name: transformers |
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language: |
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- multilingual |
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- bn |
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- cs |
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- de |
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- en |
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- et |
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- fi |
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- fr |
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- gu |
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- ha |
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- hi |
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- is |
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- ja |
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- kk |
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- km |
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- lt |
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- lv |
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- pl |
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- ps |
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- ru |
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- ta |
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- tr |
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- uk |
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- xh |
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- zh |
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- zu |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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tags: |
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- quality-estimation |
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- regression |
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- generated_from_trainer |
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datasets: |
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- ymoslem/wmt-da-human-evaluation |
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model-index: |
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- name: Quality Estimation for Machine Translation |
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results: |
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- task: |
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type: regression |
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dataset: |
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name: ymoslem/wmt-da-human-evaluation |
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type: QE |
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metrics: |
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- name: Pearson Correlation |
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type: Pearson |
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value: 0.422 |
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- name: Mean Absolute Error |
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type: MAE |
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value: 0.196 |
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- name: Root Mean Squared Error |
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type: RMSE |
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value: 0.245 |
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- name: R-Squared |
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type: R2 |
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value: 0.245 |
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metrics: |
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- perplexity |
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- mae |
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- r_squared |
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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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# Quality Estimation for Machine Translation |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the ymoslem/wmt-da-human-evaluation dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0752 |
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## Model description |
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This model is for reference-free quality estimation (QE) of machine translation (MT) systems. |
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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: 8e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- training_steps: 20000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:-----:|:---------------:| |
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| 0.0743 | 0.0502 | 1000 | 0.0598 | |
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| 0.0853 | 0.1004 | 2000 | 0.0745 | |
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| 0.0829 | 0.1506 | 3000 | 0.0726 | |
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| 0.0814 | 0.2008 | 4000 | 0.0872 | |
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| 0.0805 | 0.2509 | 5000 | 0.0715 | |
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| 0.0782 | 0.3011 | 6000 | 0.0819 | |
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| 0.0789 | 0.3513 | 7000 | 0.0733 | |
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| 0.0791 | 0.4015 | 8000 | 0.0748 | |
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| 0.0787 | 0.4517 | 9000 | 0.0759 | |
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| 0.0761 | 0.5019 | 10000 | 0.0725 | |
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| 0.0746 | 0.5521 | 11000 | 0.0745 | |
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| 0.0762 | 0.6023 | 12000 | 0.0750 | |
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| 0.077 | 0.6524 | 13000 | 0.0725 | |
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| 0.0777 | 0.7026 | 14000 | 0.0737 | |
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| 0.0764 | 0.7528 | 15000 | 0.0745 | |
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| 0.0781 | 0.8030 | 16000 | 0.0750 | |
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| 0.0748 | 0.8532 | 17000 | 0.0765 | |
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| 0.0768 | 0.9034 | 18000 | 0.0750 | |
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| 0.0737 | 0.9536 | 19000 | 0.0759 | |
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| 0.0769 | 1.0038 | 20000 | 0.0752 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.4.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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## Inference |
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1. Install the required libraries. |
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```bash |
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pip3 install --upgrade datasets accelerate transformers |
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pip3 install --upgrade flash_attn triton |
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``` |
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2. Load the test dataset. |
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```python |
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from datasets import load_dataset |
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test_dataset = load_dataset("ymoslem/wmt-da-human-evaluation", |
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split="test", |
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trust_remote_code=True |
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) |
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print(test_dataset) |
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``` |
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3. Load the model and tokenizer: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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# Load the fine-tuned model and tokenizer |
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model_name = "ymoslem/ModernBERT-large-qe-v1" |
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model = AutoModelForSequenceClassification.from_pretrained( |
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model_name, |
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device_map="auto", |
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torch_dtype=torch.bfloat16, |
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attn_implementation="flash_attention_2", |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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# Move model to GPU if available |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model.to(device) |
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model.eval() |
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``` |
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4. Prepare the dataset. Each source segment `src` and target segment `tgt` are separated by the `sep_token`, which is `'</s>'` for ModernBERT. |
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```python |
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sep_token = tokenizer.sep_token |
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input_test_texts = [f"{src} {sep_token} {tgt}" for src, tgt in zip(test_dataset["src"], test_dataset["mt"])] |
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``` |
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5. Generate predictions. |
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If you print `model.config.problem_type`, the output is `regression`. |
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Still, you can use the "text-classification" pipeline as follows (cf. [pipeline documentation](https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.TextClassificationPipeline)): |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", |
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model=model_name, |
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tokenizer=tokenizer, |
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device=0, |
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) |
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predictions = classifier(input_test_texts, |
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batch_size=128, |
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truncation=True, |
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padding="max_length", |
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max_length=tokenizer.model_max_length, |
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) |
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predictions = [prediction["score"] for prediction in predictions] |
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``` |
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Alternatively, you can use an elaborate version of the code, which is slightly faster and provides more control. |
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```python |
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from torch.utils.data import DataLoader |
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import torch |
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from tqdm.auto import tqdm |
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# Tokenization function |
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def process_batch(batch, tokenizer, device): |
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sep_token = tokenizer.sep_token |
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input_texts = [f"{src} {sep_token} {tgt}" for src, tgt in zip(batch["src"], batch["mt"])] |
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tokens = tokenizer(input_texts, |
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truncation=True, |
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padding="max_length", |
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max_length=tokenizer.model_max_length, |
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return_tensors="pt", |
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).to(device) |
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return tokens |
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# Create a DataLoader for batching |
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test_dataloader = DataLoader(test_dataset, |
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batch_size=128, # Adjust batch size as needed |
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shuffle=False) |
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# List to store all predictions |
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predictions = [] |
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with torch.no_grad(): |
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for batch in tqdm(test_dataloader, desc="Inference Progress", unit="batch"): |
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tokens = process_batch(batch, tokenizer, device) |
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# Forward pass: Generate model's logits |
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outputs = model(**tokens) |
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# Get logits (predictions) |
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logits = outputs.logits |
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# Extract the regression predicted values |
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batch_predictions = logits.squeeze() |
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# Extend the list with the predictions |
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predictions.extend(batch_predictions.tolist()) |
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``` |
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