pjbhaumik commited on
Commit
cadf223
1 Parent(s): 8f80069

Add new SentenceTransformer model.

Browse files
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
+ }
README.md ADDED
@@ -0,0 +1,130 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: sentence-transformers
3
+ pipeline_tag: sentence-similarity
4
+ tags:
5
+ - sentence-transformers
6
+ - feature-extraction
7
+ - sentence-similarity
8
+ - transformers
9
+
10
+ ---
11
+
12
+ # pjbhaumik/biencoder-finetune-model-v4
13
+
14
+ 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.
15
+
16
+ <!--- Describe your model here -->
17
+
18
+ ## Usage (Sentence-Transformers)
19
+
20
+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
21
+
22
+ ```
23
+ pip install -U sentence-transformers
24
+ ```
25
+
26
+ Then you can use the model like this:
27
+
28
+ ```python
29
+ from sentence_transformers import SentenceTransformer
30
+ sentences = ["This is an example sentence", "Each sentence is converted"]
31
+
32
+ model = SentenceTransformer('pjbhaumik/biencoder-finetune-model-v4')
33
+ embeddings = model.encode(sentences)
34
+ print(embeddings)
35
+ ```
36
+
37
+
38
+
39
+ ## Usage (HuggingFace Transformers)
40
+ 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.
41
+
42
+ ```python
43
+ from transformers import AutoTokenizer, AutoModel
44
+ import torch
45
+
46
+
47
+ #Mean Pooling - Take attention mask into account for correct averaging
48
+ def mean_pooling(model_output, attention_mask):
49
+ token_embeddings = model_output[0] #First element of model_output contains all token embeddings
50
+ input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
51
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
52
+
53
+
54
+ # Sentences we want sentence embeddings for
55
+ sentences = ['This is an example sentence', 'Each sentence is converted']
56
+
57
+ # Load model from HuggingFace Hub
58
+ tokenizer = AutoTokenizer.from_pretrained('pjbhaumik/biencoder-finetune-model-v4')
59
+ model = AutoModel.from_pretrained('pjbhaumik/biencoder-finetune-model-v4')
60
+
61
+ # Tokenize sentences
62
+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
63
+
64
+ # Compute token embeddings
65
+ with torch.no_grad():
66
+ model_output = model(**encoded_input)
67
+
68
+ # Perform pooling. In this case, mean pooling.
69
+ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
70
+
71
+ print("Sentence embeddings:")
72
+ print(sentence_embeddings)
73
+ ```
74
+
75
+
76
+
77
+ ## Evaluation Results
78
+
79
+ <!--- Describe how your model was evaluated -->
80
+
81
+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=pjbhaumik/biencoder-finetune-model-v4)
82
+
83
+
84
+ ## Training
85
+ The model was trained with the parameters:
86
+
87
+ **DataLoader**:
88
+
89
+ `torch.utils.data.dataloader.DataLoader` of length 469 with parameters:
90
+ ```
91
+ {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
92
+ ```
93
+
94
+ **Loss**:
95
+
96
+ `sentence_transformers.losses.MultipleNegativesSymmetricRankingLoss.MultipleNegativesSymmetricRankingLoss` with parameters:
97
+ ```
98
+ {'scale': 20.0, 'similarity_fct': 'cos_sim'}
99
+ ```
100
+
101
+ Parameters of the fit()-Method:
102
+ ```
103
+ {
104
+ "epochs": 12,
105
+ "evaluation_steps": 100,
106
+ "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
107
+ "max_grad_norm": 1,
108
+ "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
109
+ "optimizer_params": {
110
+ "lr": 2e-05
111
+ },
112
+ "scheduler": "WarmupLinear",
113
+ "steps_per_epoch": null,
114
+ "warmup_steps": 100,
115
+ "weight_decay": 0.01
116
+ }
117
+ ```
118
+
119
+
120
+ ## Full Model Architecture
121
+ ```
122
+ SentenceTransformer(
123
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
124
+ (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
125
+ )
126
+ ```
127
+
128
+ ## Citing & Authors
129
+
130
+ <!--- Describe where people can find more information -->
config.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "distilbert-base-uncased",
3
+ "activation": "gelu",
4
+ "architectures": [
5
+ "DistilBertModel"
6
+ ],
7
+ "attention_dropout": 0.1,
8
+ "dim": 768,
9
+ "dropout": 0.1,
10
+ "hidden_dim": 3072,
11
+ "initializer_range": 0.02,
12
+ "max_position_embeddings": 512,
13
+ "model_type": "distilbert",
14
+ "n_heads": 12,
15
+ "n_layers": 6,
16
+ "pad_token_id": 0,
17
+ "qa_dropout": 0.1,
18
+ "seq_classif_dropout": 0.2,
19
+ "sinusoidal_pos_embds": false,
20
+ "tie_weights_": true,
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.40.1",
23
+ "vocab_size": 30522
24
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.7.0",
4
+ "transformers": "4.40.1",
5
+ "pytorch": "2.1.0"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null
9
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:51db4f2b93d457e9a4369bd9acda67aa87aeb75232a256025dce1fbf49649cad
3
+ size 265462608
modules.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ }
14
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "mask_token": "[MASK]",
4
+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "100": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "101": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "102": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "103": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": true,
47
+ "mask_token": "[MASK]",
48
+ "model_max_length": 1000000000000000019884624838656,
49
+ "pad_token": "[PAD]",
50
+ "sep_token": "[SEP]",
51
+ "strip_accents": null,
52
+ "tokenize_chinese_chars": true,
53
+ "tokenizer_class": "DistilBertTokenizer",
54
+ "unk_token": "[UNK]"
55
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff