Downloads · 30 days
16
1% of all-time downloads
dpr-question_encoder_bert_uncased_L-2_H-128_A-2
nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2
dpr-question_encoder_bert_uncased_L-2_H-128_A-2 is a feature extraction model from nlpconnect. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as apache-2.0.
probably proofread and complete it, then remove this comment. --
All-time downloads
3K
Public
Repo size
52.7 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.h517.6 MB · 49%
At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Dec 29, 2021
- SHA
- c8fc8506
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
dpr-question_encoder_bert_uncased_L-2_H-128_A-2
This model(google/bert_uncased_L-2_H-128_A-2) was trained from scratch on training data: data.retriever.nq-adv-hn-train(facebookresearch/DPR). It achieves the following results on the evaluation set:
Evaluation data
evaluation dataset: facebook-dpr-dev-dataset from official DPR github
| model_name | data_name | num of queries | num of passages | R@10 | R@20 | R@50 | R@100 | R@100 |
|---|---|---|---|---|---|---|---|---|
| nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2(our) | nq-dev dataset | 6445 | 199795 | 60.53% | 68.28% | 76.07% | 80.98% | 91.45% |
| nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2(our) | nq-dev dataset | 6445 | 199795 | 65.43% | 71.99% | 79.03% | 83.24% | 92.11% |
| *facebook/dpr-ctx_encoder-single-nq-base(hf/fb) | nq-dev dataset | 6445 | 199795 | 40.94% | 49.27% | 59.05% | 66.00% | 82.00% |
evaluation dataset: UKPLab/beir test data but we have used first 2lac passage only.
| model_name | data_name | num of queries | num of passages | R@10 | R@20 | R@50 | R@100 | R@100 |
|---|---|---|---|---|---|---|---|---|
| nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2(our) | nq-test dataset | 3452 | 200001 | 49.68% | 59.06% | 69.40% | 75.75% | 89.28% |
| nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2(our) | nq-test dataset | 3452 | 200001 | 51.62% | 61.09% | 70.10% | 76.07% | 88.70% |
| *facebook/dpr-ctx_encoder-single-nq-base(hf/fb) | nq-test dataset | 3452 | 200001 | 32.93% | 43.74% | 56.95% | 66.30% | 83.92% |
Note: * means we have evaluated on same eval dataset.
Usage (HuggingFace Transformers)
passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2")
query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-12_H-128_A-2")
p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2")
q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-12_H-128_A-2")
def get_title_text_combined(passage_dicts):
res = []
for p in passage_dicts:
res.append(tuple((p['title'], p['text'])))
return res
processed_passages = get_title_text_combined(passage_dicts)
def extracted_passage_embeddings(processed_passages, model_config):
passage_inputs = tokenizer.batch_encode_plus(
processed_passages,
add_special_tokens=True,
truncation=True,
padding="max_length",
max_length=model_config.passage_max_seq_len,
return_token_type_ids=True
)
passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']),
np.array(passage_inputs['attention_mask']),
np.array(passage_inputs['token_type_ids'])],
batch_size=512,
verbose=1)
return passage_embeddings
passage_embeddings = extracted_passage_embeddings(processed_passages, model_config)
def extracted_query_embeddings(queries, model_config):
query_inputs = tokenizer.batch_encode_plus(
queries,
add_special_tokens=True,
truncation=True,
padding="max_length",
max_length=model_config.query_max_seq_len,
return_token_type_ids=True
)
query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']),
np.array(query_inputs['attention_mask']),
np.array(query_inputs['token_type_ids'])],
batch_size=512,
verbose=1)
return query_embeddings
query_embeddings = extracted_query_embeddings(queries, model_config)
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Framework versions
- Transformers 4.15.0
- TensorFlow 2.7.0
- Tokenizers 0.10.3
- Task
- Feature Extraction
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Dec 29, 2021