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--- |
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pipeline_tag: sentence-similarity |
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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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--- |
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This model utilizes a newer version of Sentence Transformers. If you're having trouble using this model, please try installing the latest version of Sentence Transformers with: |
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```bash |
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pip install --upgrade --force-reinstall --no-deps git+https://github.com/UKPLab/sentence-transformers.git |
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``` |
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# carles-undergrad-thesis/indobert-mmarco-hardnegs-bm25 |
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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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<!--- Describe your model here --> |
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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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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('carles-undergrad-thesis/indobert-mmarco-hardnegs-bm25') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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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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def cls_pooling(model_output, attention_mask): |
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return model_output[0][:,0] |
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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('carles-undergrad-thesis/indobert-mmarco-hardnegs-bm25') |
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model = AutoModel.from_pretrained('carles-undergrad-thesis/indobert-mmarco-hardnegs-bm25') |
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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, cls pooling. |
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sentence_embeddings = cls_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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| Model | Mmarco Dev | | MrTyDi Test | | Miracal Test | | |
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|-----------------------------------------|------------|----------------|-------------|----------------|--------------|----------------------------| |
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| | MRR@10 | R@1000 | MRR@10 | R@1000 | NCDG@10 | R@1K | |
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| $\text{BM25 (Elastic Search)}$ | .114 | .642 | .279 | .858 | .391 | .971 | |
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| $\text{IndoBERT}_{\text{DOTHardnegs}}$ | .232 | .847 | .471 | .921 | .397 | .898 | |
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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 15593 with parameters: |
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``` |
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{'batch_size': 32, '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.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: |
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``` |
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{'scale': 1, 'similarity_fct': 'dot_score'} |
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``` |
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Parameters of the fit()-Method: |
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``` |
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{ |
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"epochs": 5, |
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"evaluation_steps": 10000, |
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"evaluator": "NoneType", |
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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': 250, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) |
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) |
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``` |
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## Citing & Authors |
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<!--- Describe where people can find more information --> |
