Upload 11 files
Browse files- 1_Pooling/config.json +10 -0
- README.md +125 -1
- config.json +25 -0
- config_sentence_transformers.json +9 -0
- model.safetensors +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +44 -0
- tokenizer.json +0 -0
- tokenizer_config.json +71 -0
- vocab.txt +0 -0
1_Pooling/config.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"word_embedding_dimension": 384,
|
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
CHANGED
@@ -1,3 +1,127 @@
|
|
1 |
---
|
2 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
# {MODEL_NAME}
|
13 |
+
|
14 |
+
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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('{MODEL_NAME}')
|
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('{MODEL_NAME}')
|
59 |
+
model = AutoModel.from_pretrained('{MODEL_NAME}')
|
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={MODEL_NAME})
|
82 |
+
|
83 |
+
|
84 |
+
## Training
|
85 |
+
The model was trained with the parameters:
|
86 |
+
|
87 |
+
**DataLoader**:
|
88 |
+
|
89 |
+
`torch.utils.data.dataloader.DataLoader` of length 113 with parameters:
|
90 |
+
```
|
91 |
+
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
|
92 |
+
```
|
93 |
+
|
94 |
+
**Loss**:
|
95 |
+
|
96 |
+
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
|
97 |
+
|
98 |
+
Parameters of the fit()-Method:
|
99 |
+
```
|
100 |
+
{
|
101 |
+
"epochs": 1,
|
102 |
+
"evaluation_steps": 1000,
|
103 |
+
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
|
104 |
+
"max_grad_norm": 1,
|
105 |
+
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
|
106 |
+
"optimizer_params": {
|
107 |
+
"lr": 2e-05
|
108 |
+
},
|
109 |
+
"scheduler": "WarmupLinear",
|
110 |
+
"steps_per_epoch": null,
|
111 |
+
"warmup_steps": 500,
|
112 |
+
"weight_decay": 0.01
|
113 |
+
}
|
114 |
+
```
|
115 |
+
|
116 |
+
|
117 |
+
## Full Model Architecture
|
118 |
+
```
|
119 |
+
SentenceTransformer(
|
120 |
+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
121 |
+
(1): Pooling({'word_embedding_dimension': 384, '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})
|
122 |
+
)
|
123 |
+
```
|
124 |
+
|
125 |
+
## Citing & Authors
|
126 |
+
|
127 |
+
<!--- Describe where people can find more information -->
|
config.json
ADDED
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "Mihaiii/test8",
|
3 |
+
"architectures": [
|
4 |
+
"BertModel"
|
5 |
+
],
|
6 |
+
"attention_probs_dropout_prob": 0.1,
|
7 |
+
"classifier_dropout": null,
|
8 |
+
"hidden_act": "gelu",
|
9 |
+
"hidden_dropout_prob": 0.1,
|
10 |
+
"hidden_size": 384,
|
11 |
+
"initializer_range": 0.02,
|
12 |
+
"intermediate_size": 1536,
|
13 |
+
"layer_norm_eps": 1e-12,
|
14 |
+
"max_position_embeddings": 512,
|
15 |
+
"model_type": "bert",
|
16 |
+
"num_attention_heads": 12,
|
17 |
+
"num_hidden_layers": 1,
|
18 |
+
"pad_token_id": 0,
|
19 |
+
"position_embedding_type": "absolute",
|
20 |
+
"torch_dtype": "float32",
|
21 |
+
"transformers_version": "4.40.1",
|
22 |
+
"type_vocab_size": 2,
|
23 |
+
"use_cache": true,
|
24 |
+
"vocab_size": 30522
|
25 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.2.2",
|
4 |
+
"transformers": "4.34.0",
|
5 |
+
"pytorch": "2.0.1+cu118"
|
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 sha256:682abffb0e7febd3ea7722b88cb9ca94cfa9f113a9fa1d01574c702f4e49b9f7
|
3 |
+
size 55366056
|
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 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"[PAD]",
|
4 |
+
"[UNK]",
|
5 |
+
"[CLS]",
|
6 |
+
"[SEP]",
|
7 |
+
"[MASK]"
|
8 |
+
],
|
9 |
+
"cls_token": {
|
10 |
+
"content": "[CLS]",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": false,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"mask_token": {
|
17 |
+
"content": "[MASK]",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"pad_token": {
|
24 |
+
"content": "[PAD]",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"sep_token": {
|
31 |
+
"content": "[SEP]",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"unk_token": {
|
38 |
+
"content": "[UNK]",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": false,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false
|
43 |
+
}
|
44 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
"additional_special_tokens": [
|
45 |
+
"[PAD]",
|
46 |
+
"[UNK]",
|
47 |
+
"[CLS]",
|
48 |
+
"[SEP]",
|
49 |
+
"[MASK]"
|
50 |
+
],
|
51 |
+
"clean_up_tokenization_spaces": true,
|
52 |
+
"cls_token": "[CLS]",
|
53 |
+
"do_basic_tokenize": true,
|
54 |
+
"do_lower_case": true,
|
55 |
+
"mask_token": "[MASK]",
|
56 |
+
"max_length": 128,
|
57 |
+
"model_max_length": 512,
|
58 |
+
"never_split": null,
|
59 |
+
"pad_to_multiple_of": null,
|
60 |
+
"pad_token": "[PAD]",
|
61 |
+
"pad_token_type_id": 0,
|
62 |
+
"padding_side": "right",
|
63 |
+
"sep_token": "[SEP]",
|
64 |
+
"stride": 0,
|
65 |
+
"strip_accents": null,
|
66 |
+
"tokenize_chinese_chars": true,
|
67 |
+
"tokenizer_class": "BertTokenizer",
|
68 |
+
"truncation_side": "right",
|
69 |
+
"truncation_strategy": "longest_first",
|
70 |
+
"unk_token": "[UNK]"
|
71 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|