Xlsim En De

🚀 {AnanyaCoder/XLsim_en-de}

XLSim is a Machine Translation (MT) evaluation metric based on a Siamese architecture. It's a supervised reference - based metric that regresses on human scores from WMT (2017 - 2022). By leveraging the cross - lingual language model XLM - RoBERTa - base [ https://huggingface.co/xlm - roberta - base ], we train a supervised model using a Siamese network with CosineSimilarityLoss.

🚀 Quick Start

📦 Installation

Using this model becomes easy when you have sentence - transformers installed. You can install it with the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

Once installed, you can use the model like this:

from sentence_transformers import SentenceTransformer,losses, models, util

metric_model = SentenceTransformer('{MODEL_NAME}')

#Compute embedding for both lists
mt_samples = ['This is a mt sentence1','This is a mt sentence2']
ref_samples = ['This is a ref sentence1','This is a ref sentence2']

mtembeddings = metric_model.encode(mt_samples, convert_to_tensor=True)
refembeddings = metric_model.encode(ref_samples, convert_to_tensor=True)

#Compute cosine - similarities
cosine_scores_refmt = util.cos_sim(mtembeddings, refembeddings)
#cosine_scores_srcmt = util.cos_sim(mtembeddings, srcembeddings) #qe
metric_model_scores = []
for i in range(len(mt_samples)):
    metric_model_scores.append(cosine_scores_refmt[i][i].tolist())

scores = metric_model_scores

📚 Documentation

Evaluation Results

For an automated evaluation of this model, see: [WMT23 Metrics Shared Task findings](https://aclanthology.org/2023.wmt - 1.51.pdf)

Training

The model was trained with the following parameters:

DataLoader: torch.utils.data.dataloader.DataLoader of length 6625 with parameters:

{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}

Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss

Parameters of the fit() - Method:

{
    "epochs": 4,
    "evaluation_steps": 1000,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e - 05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 2650,
    "weight_decay": 0.01
}

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors

For more information, refer to [MEE4 and XLsim : IIIT HYD’s Submissions’ for WMT23 Metrics Shared Task](https://aclanthology.org/2023.wmt - 1.66) (Mukherjee & Shrivastava, WMT 2023)