iliemihai
/

romanian-sentence-bert-base-uncased-v1

@@ -1,129 +1,94 @@
 ---
-pipeline_tag: sentence-similarity
 tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
 ---
-# {MODEL_NAME}
-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.
-<!--- Describe your model here -->
-## Usage (Sentence-Transformers)
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-```
-pip install -U sentence-transformers
-```
-Then you can use the model like this:
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-## Usage (HuggingFace Transformers)
-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.
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
-#Mean Pooling - Take attention mask into account for correct averaging
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
-# Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-# Perform pooling. In this case, mean pooling.
-sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-## Evaluation Results
-<!--- Describe how your model was evaluated -->
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-## Training
-The model was trained with the parameters:
-**DataLoader**:
-`torch.utils.data.dataloader.DataLoader` of length 8047 with parameters:
 ```
-{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
-**Loss**:
-`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
-  ```
-  {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
-  ```
-Parameters of the fit()-Method:
 ```
-{
-    "epochs": 10,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 8047,
-    "weight_decay": 0.01
 }
 ```
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
-  (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
-<!--- Describe where people can find more information -->

 ---
+language: ro
 tags:
+- bert
+- fill-mask
+license: mit
 ---
+# bert-base-romanian-uncased-v1
+The BERT **base**, **uncased** model for Romanian, trained on a 15GB corpus, version ![v1.0](https://img.shields.io/badge/v1.0-21%20Apr%202020-ff6666)
+### How to use
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch
+# load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("iliemihai/sentence-bert-base-romanian-uncased-v1", do_lower_case=True)
+model = AutoModel.from_pretrained("dumitrescustefan/bert-base-romanian-uncased-v1")
+# tokenize a sentence and run through the model
+input_ids = torch.tensor(tokenizer.encode("Acesta este un test.", add_special_tokens=True)).unsqueeze(0)  # Batch size 1
+outputs = model(input_ids)
+# get encoding
+last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
+```
+Remember to always sanitize your text! Replace ``s`` and ``t`` cedilla-letters to comma-letters with :
+```
+text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș")
+```
+because the model was **NOT** trained on cedilla ``s`` and ``t``s. If you don't, you will have decreased performance due to ``<UNK>``s and increased number of tokens per word.
+### Evaluation
+Evaluation is performed on Romaian STSb dataset
+| Model                          |  Spearman |  Pearson  |
+|--------------------------------|:-----:|:------:|
+| bert-base-romanian-uncased-v1 | 0.8086 |  	0.8159 |
+| sentence-bert-base-romanian-uncased-v1  | **0.84** |  **0.84** |
+### Corpus
+#### Pretraining
+The model is trained on the following corpora (stats in the table below are after cleaning):
+| Corpus    	| Lines(M) 	| Words(M) 	| Chars(B) 	| Size(GB) 	|
+|-----------|:--------:|:--------:|:--------:|:--------:|
+| OPUS      	|   55.05  	|  635.04  	|   4.045  	|    3.8   	|
+| OSCAR     	|   33.56  	|  1725.82 	|  11.411  	|    11    	|
+| Wikipedia 	|   1.54   	|   60.47  	|   0.411  	|    0.4   	|
+| **Total**     	|   **90.15**  	|  **2421.33** 	|  **15.867**  	|   **15.2**   	|
+#### Finetuning
+The model is finetune on the  RO_MNLI dataset (translated entire MNLI dataset from English to Romanian).
+### Citation
+If you use this model in a research paper, I'd kindly ask you to cite the following paper:
 ```
+Stefan Dumitrescu, Andrei-Marius Avram, and Sampo Pyysalo. 2020. The birth of Romanian BERT. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4324–4328, Online. Association for Computational Linguistics.
 ```
+or, in bibtex:
 ```
+@inproceedings{dumitrescu-etal-2020-birth,
+    title = "The birth of {R}omanian {BERT}",
+    author = "Dumitrescu, Stefan  and
+      Avram, Andrei-Marius  and
+      Pyysalo, Sampo",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.findings-emnlp.387",
+    doi = "10.18653/v1/2020.findings-emnlp.387",
+    pages = "4324--4328",
 }
 ```
+#### Acknowledgements
+- We'd like to thank [Sampo Pyysalo](https://github.com/spyysalo) from TurkuNLP for helping us out with the compute needed to pretrain the v1.0 BERT models. He's awesome!