185 lines
6.8 KiB
Markdown
185 lines
6.8 KiB
Markdown
---
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language:
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- en
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- el
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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widget:
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- source_sentence: "Το κινητό έπεσε και έσπασε."
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sentences: [
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"H πτώση κατέστρεψε τη συσκευή.",
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"Το αυτοκίνητο έσπασε στα δυο.",
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"Ο υπουργός έπεσε και έσπασε το πόδι του."
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]
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pipeline_tag: sentence-similarity
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license: apache-2.0
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---
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# Semantic Textual Similarity for the Greek language using Transformers and Transfer Learning
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### By the Hellenic Army Academy (SSE) and the Technical University of Crete (TUC)
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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We follow a Teacher-Student transfer learning approach described [here](https://www.sbert.net/examples/training/multilingual/README.html) to train an XLM-Roberta-base model on STS using parallel EN-EL sentence pairs.
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('{MODEL_NAME}')
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sentences1 = ['Το κινητό έπεσε και έσπασε.',
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'Το κινητό έπεσε και έσπασε.',
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'Το κινητό έπεσε και έσπασε.']
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sentences2 = ["H πτώση κατέστρεψε τη συσκευή.",
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"Το αυτοκίνητο έσπασε στα δυο.",
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"Ο υπουργός έπεσε και έσπασε το πόδι του."]
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embeddings1 = model.encode(sentences1, convert_to_tensor=True)
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embeddings2 = model.encode(sentences2, convert_to_tensor=True)
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#Compute cosine-similarities (clone repo for util functions)
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from sentence_transformers import util
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cosine_scores = util.pytorch_cos_sim(embeddings1, embeddings2)
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#Output the pairs with their score
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for i in range(len(sentences1)):
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print("{} {} Score: {:.4f}".format(sentences1[i], sentences2[i], cosine_scores[i][i]))
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#Outputs:
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#Το κινητό έπεσε και έσπασε. H πτώση κατέστρεψε τη συσκευή. Score: 0.6741
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#Το κινητό έπεσε και έσπασε. Το αυτοκίνητο έσπασε στα δυο. Score: 0.5067
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#Το κινητό έπεσε και έσπασε. Ο υπουργός έπεσε και έσπασε το πόδι του. Score: 0.4548
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```
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## Usage (HuggingFace Transformers)
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Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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#Mean Pooling - Take attention mask into account for correct averaging
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def mean_pooling(model_output, attention_mask):
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
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# Sentences we want sentence embeddings for
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sentences = ['This is an example sentence', 'Each sentence is converted']
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
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model = AutoModel.from_pretrained(
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, max pooling.
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sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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print("Sentence embeddings:")
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print(sentence_embeddings)
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```
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## Evaluation Results
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#### Similarity Evaluation on STS.en-el.txt (translated manually for evaluation purposes)
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We measure the semantic textual similarity (STS) between sentence pairs in different languages:
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| cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman |
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| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
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0.834474802920369 | 0.845687403828107 | 0.815895882192263 | 0.81084300966291 | 0.816333562677654 | 0.813879742416394 | 0.7945167996031 | 0.802604238383742 |
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#### Translation
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We measure the translation accuracy. Given a list with source sentences, for example, 1000 English sentences. And a list with matching target (translated) sentences, for example, 1000 Greek sentences. For each sentence pair, we check if their embeddings are the closest using cosine similarity. I.e., for each src_sentences[i] we check if trg_sentences[i] has the highest similarity out of all target sentences. If this is the case, we have a hit, otherwise an error. This evaluator reports accuracy (higher = better).
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| src2trg | trg2src |
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| ----------- | ----------- |
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| 0.981 | 0.9775 |
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 135121 with parameters:
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```
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{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.MSELoss.MSELoss`
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Parameters of the fit()-Method:
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```
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{
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"callback": null,
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"epochs": 4,
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"evaluation_steps": 1000,
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"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'transformers.optimization.AdamW'>",
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"optimizer_params": {
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"correct_bias": false,
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"eps": 1e-06,
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 10000,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 400, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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(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})
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)
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```
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## Acknowledgement
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The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
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## Citing & Authors
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Citation info for Greek model: TBD
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Based on the transfer learning approach of [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/abs/2004.09813)
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