Sigurdur commited on
Commit
c682154
1 Parent(s): fa73879

Upload 10 files

Browse files
README.md CHANGED
@@ -1,20 +1,123 @@
1
- # ISL-SBERT small
 
 
 
 
 
 
2
 
3
- Sentence transformer, trained using unsupervised technique, TSDAE. The models take in an Icelandic text and creates sentence embeddings from it.
4
 
5
- Based off of this [article](https://www.pinecone.io/learn/unsupervised-training-sentence-transformers/)
6
 
 
7
 
8
- ## Data
9
 
10
- the model was trained on 100_000 sentences selected at random from clarin-is: [unanotated news2 from IGC(RMH)](https://repository.clarin.is/repository/xmlui/handle/20.500.12537/238)
11
 
12
- to install the data, run the following command:
13
 
14
- ```bash
15
- curl --remote-name-all https://repository.clarin.is/repository/xmlui/bitstream/handle/20.500.12537/238{/IGC-News2-22.10.TEI.zip}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
16
  ```
17
 
18
- ## Author
19
 
20
- Sigurður Haukur Birgisson
 
1
+ ---
2
+ pipeline_tag: sentence-similarity
3
+ tags:
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
7
+ - transformers
8
 
9
+ ---
10
 
11
+ # {MODEL_NAME}
12
 
13
+ 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.
14
 
15
+ <!--- Describe your model here -->
16
 
17
+ ## Usage (Sentence-Transformers)
18
 
19
+ Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
20
 
21
+ ```
22
+ pip install -U sentence-transformers
23
+ ```
24
+
25
+ Then you can use the model like this:
26
+
27
+ ```python
28
+ from sentence_transformers import SentenceTransformer
29
+ sentences = ["This is an example sentence", "Each sentence is converted"]
30
+
31
+ model = SentenceTransformer('{MODEL_NAME}')
32
+ embeddings = model.encode(sentences)
33
+ print(embeddings)
34
+ ```
35
+
36
+
37
+
38
+ ## Usage (HuggingFace Transformers)
39
+ 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.
40
+
41
+ ```python
42
+ from transformers import AutoTokenizer, AutoModel
43
+ import torch
44
+
45
+
46
+ def cls_pooling(model_output, attention_mask):
47
+ return model_output[0][:,0]
48
+
49
+
50
+ # Sentences we want sentence embeddings for
51
+ sentences = ['This is an example sentence', 'Each sentence is converted']
52
+
53
+ # Load model from HuggingFace Hub
54
+ tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
55
+ model = AutoModel.from_pretrained('{MODEL_NAME}')
56
+
57
+ # Tokenize sentences
58
+ encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
59
+
60
+ # Compute token embeddings
61
+ with torch.no_grad():
62
+ model_output = model(**encoded_input)
63
+
64
+ # Perform pooling. In this case, cls pooling.
65
+ sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
66
+
67
+ print("Sentence embeddings:")
68
+ print(sentence_embeddings)
69
+ ```
70
+
71
+
72
+
73
+ ## Evaluation Results
74
+
75
+ <!--- Describe how your model was evaluated -->
76
+
77
+ For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
78
+
79
+
80
+ ## Training
81
+ The model was trained with the parameters:
82
+
83
+ **DataLoader**:
84
+
85
+ `torch.utils.data.dataloader.DataLoader` of length 100000 with parameters:
86
+ ```
87
+ {'batch_size': 1, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
88
+ ```
89
+
90
+ **Loss**:
91
+
92
+ `sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss`
93
+
94
+ Parameters of the fit()-Method:
95
+ ```
96
+ {
97
+ "epochs": 1,
98
+ "evaluation_steps": 0,
99
+ "evaluator": "NoneType",
100
+ "max_grad_norm": 1,
101
+ "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
102
+ "optimizer_params": {
103
+ "lr": 3e-05
104
+ },
105
+ "scheduler": "constantlr",
106
+ "steps_per_epoch": null,
107
+ "warmup_steps": 10000,
108
+ "weight_decay": 0
109
+ }
110
+ ```
111
+
112
+
113
+ ## Full Model Architecture
114
+ ```
115
+ SentenceTransformer(
116
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
117
+ (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})
118
+ )
119
  ```
120
 
121
+ ## Citing & Authors
122
 
123
+ <!--- Describe where people can find more information -->
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "bert-base-uncased",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "gradient_checkpointing": false,
9
+ "hidden_act": "gelu",
10
+ "hidden_dropout_prob": 0.1,
11
+ "hidden_size": 768,
12
+ "initializer_range": 0.02,
13
+ "intermediate_size": 3072,
14
+ "layer_norm_eps": 1e-12,
15
+ "max_position_embeddings": 512,
16
+ "model_type": "bert",
17
+ "num_attention_heads": 12,
18
+ "num_hidden_layers": 12,
19
+ "pad_token_id": 0,
20
+ "position_embedding_type": "absolute",
21
+ "torch_dtype": "float32",
22
+ "transformers_version": "4.26.0.dev0",
23
+ "type_vocab_size": 2,
24
+ "use_cache": true,
25
+ "vocab_size": 30522
26
+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "2.2.2",
4
+ "transformers": "4.26.0.dev0",
5
+ "pytorch": "2.0.1+cu117"
6
+ }
7
+ }
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
+ ]
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:296ff5209c73dd116ee83f8b8f42e81de65ff1f75b6b4711771bbdbcc6bd184a
3
+ size 438000173
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 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "do_lower_case": true,
4
+ "mask_token": "[MASK]",
5
+ "model_max_length": 512,
6
+ "name_or_path": "bert-base-uncased",
7
+ "pad_token": "[PAD]",
8
+ "sep_token": "[SEP]",
9
+ "special_tokens_map_file": null,
10
+ "strip_accents": null,
11
+ "tokenize_chinese_chars": true,
12
+ "tokenizer_class": "BertTokenizer",
13
+ "unk_token": "[UNK]"
14
+ }
vocab.txt ADDED
The diff for this file is too large to render. See raw diff