Repository: Unbabel/COMET
Issue: Comet Evaluation Metric Error with Command line Utility 
Root cause: External Dependency Breakage

## 🐛 Bug

Env: AWS SageMaker Jupyter Notebook 

Comet Code: 
!comet-score -s {src_file} -t Candidate_file -r {ref_file}

Error along with output: 
Seed set to 1
Fetching 5 files: 100%|████████████████████████| 5/5 [00:00<00:00, 73071.50it/s]
Lightning automatically upgraded your loaded checkpoint from v1.8.3.post1 to v2.1.0. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint ../.cache/huggingface/hub/models--Unbabel--wmt22-comet-da/snapshots/371e9839ca4e213dde891b066cf3080f75ec7e72/checkpoints/model.ckpt`
Encoder model frozen.
/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/core/saving.py:177: Found keys that are not in the model state dict but in the checkpoint: ['encoder.model.embeddings.position_ids']
/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
Predicting DataLoader 0:   0%|                           | 0/94 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/home/ec2-user/anaconda3/envs/python3/bin/comet-score", line 8, in <module>
    sys.exit(score_command())
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/comet/cli/score.py", line 225, in score_command
    outputs = model.predict(
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/comet/models/base.py", line 627, in predict
    predictions = trainer.predict(
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 865, in predict
    return call._call_and_handle_interrupt(
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 44, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 904, in _predict_impl
    results = self._run(model, ckpt_path=ckpt_path)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 990, in _run
    results = self._run_stage()
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1031, in _run_stage
    return self.predict_loop.run()
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/loops/utilities.py", line 181, in _decorator
    return loop_run(self, *args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py", line 122, in run
    self._predict_step(batch, batch_idx, dataloader_idx, dataloader_iter)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py", line 250, in _predict_step
    predictions = call._call_strategy_hook(trainer, "predict_step", *step_args)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 309, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 429, in predict_step
    return self.lightning_module.predict_step(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/comet/models/base.py", line 430, in predict_step
    model_outputs = Prediction(scores=self(**batch).score)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/comet/models/regression/regression_metric.py", line 273, in forward
    return self.estimate(src_sentemb, mt_sentemb, ref_sentemb)
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/comet/models/regression/regression_metric.py", line 245, in estimate
    return Prediction(score=self.estimator(embedded_sequences).view(-1))
  File "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/transformers/utils/generic.py", line 327, in __init__
    raise TypeError(
TypeError: comet.models.utils.Prediction is not a dataclasss. This is a subclass of ModelOutput and so must use the @dataclass decorator.

It was working earlier for many weeks but with last week its failing with above error. 
Even out of the box, Comet sample code is failing with the same error. 

### To Reproduce 
Code form: 
https://github.com/Unbabel/COMET

echo -e "10 到 15 分钟可以送到吗\nPode ser entregue dentro de 10 a 15 minutos?" >> src.txt
echo -e "Can I receive my food in 10 to 15 minutes?\nCan it be delivered in 10 to 15 minutes?" >> hyp1.txt
echo -e "Can it be delivered within 10 to 15 minutes?\nCan you send it for 10 to 15 minutes?" >> hyp2.txt
echo -e "Can it be delivered between 10 to 15 minutes?\nCan it be delivered between 10 to 15 minutes?" >> ref.txt

comet-score -s src.txt -t hyp1.txt -r ref.txt

The code fail & give the error as 
TypeError: comet.models.utils.Prediction is not a dataclasss. This is a subclass of ModelOutput and so must use the @dataclass decorator.

### Expected behaviour
Predicting DataLoader 0: 100%|████████████████| 188/188 [08:53<00:00,  2.84s/it]
Candidate_file	Segment 0	score: 0.8637
Candidate_file	Segment 1	score: 0.5772

This is how the output should come. 

### Screenshots
![image](https://github.com/Unbabel/COMET/assets/10530263/ceff90b9-bdae-46e2-bf31-ec82b031736c)


### Environment
OS: AWS Sagemaker Jupyter Notebook on ml.m5.xlarge (4 CPU, 16 GB Memory)
Packaging 
#!pip install --upgrade pip -q
!pip install unbabel-comet
Version
pytorch-lightning<3.0.0,>=2.0.0->unbabel-comet) (1.3.1)


### Additional context
The code was working fine for many weeks but last week, the code failed everywhere including the Google Colab notebook. 


This issue arose because Hugging Face made changes to its ModelOutput class, which served as the parent class for both the Prediction and Target classes within our framework. In the latest release, I replicated the previous ModelOutput definition from Hugging Face and (hopefully) resolved the problem. Please give it a try.