Add new SentenceTransformer model.
Browse files- .gitattributes +1 -0
- 1_Pooling/config.json +7 -0
- 2_Dense/config.json +1 -0
- 2_Dense/pytorch_model.bin +3 -0
- README.md +87 -0
- config.json +27 -0
- config_sentence_transformers.json +7 -0
- eval/similarity_evaluation_results.csv +61 -0
- modules.json +20 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- similarity_evaluation_sts-test_results.csv +2 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
.gitattributes
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@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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pytorch_model.bin filter=lfs diff=lfs merge=lfs -text
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1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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2_Dense/config.json
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{"in_features": 768, "out_features": 256, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}
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2_Dense/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:f1edfe4ccb592c5f55e9bc6a8760f2cf5cfe78c60795a180d6146b82c37c35a6
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size 788519
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README.md
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---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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---
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# {MODEL_NAME}
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('{MODEL_NAME}')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Evaluation Results
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<!--- Describe how your model was evaluated -->
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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## Training
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The model was trained with the parameters:
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**DataLoader**:
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`torch.utils.data.dataloader.DataLoader` of length 11 with parameters:
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```
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{'batch_size': 15, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
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```
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**Loss**:
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`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
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Parameters of the fit()-Method:
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```
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{
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"epochs": 5,
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"evaluation_steps": 1,
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"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
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"max_grad_norm": 1,
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"optimizer_class": "<class 'transformers.optimization.AdamW'>",
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"optimizer_params": {
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"lr": 2e-05
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},
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"scheduler": "WarmupLinear",
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"steps_per_epoch": null,
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"warmup_steps": 6,
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"weight_decay": 0.01
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}
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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(2): Dense({'in_features': 768, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
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)
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```
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## Citing & Authors
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<!--- Describe where people can find more information -->
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config.json
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{
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"_name_or_path": "dccuchile/bert-base-spanish-wwm-uncased",
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 1,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.16.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31002
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.2.0",
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"transformers": "4.16.2",
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"pytorch": "1.10.0+cu111"
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}
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}
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eval/similarity_evaluation_results.csv
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3,3,0.4970640334022354,0.6357557871307151,0.5199934389415214,0.6611860186159437,0.5437755341932087,0.6586429954674208,0.47640903453148503,0.6281267176851465
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41 |
+
3,4,0.4861248809657005,0.6395703218534994,0.5104907087025273,0.6395703218534994,0.5314095598343486,0.6586429954674208,0.46968607741258545,0.6281267176851465
|
42 |
+
3,5,0.4776651349648999,0.6395703218534994,0.502607365784013,0.6395703218534994,0.5220942443266136,0.6332127639821923,0.4653095942277782,0.6065110209227021
|
43 |
+
3,6,0.471453669858453,0.6421133450020222,0.49688986688163733,0.6204976482395779,0.5154130834063639,0.6332127639821923,0.4617445979012138,0.6065110209227021
|
44 |
+
3,7,0.4634131093724732,0.6459278797248065,0.49118514511475925,0.6243121829623621,0.5091173418722049,0.6332127639821923,0.4563166691020609,0.6065110209227021
|
45 |
+
3,8,0.4550051162606645,0.638298810279238,0.48620325231107847,0.6166831135167936,0.5028983623137382,0.6332127639821923,0.4493061002642051,0.5899813704573036
|
46 |
+
3,9,0.44505538996753186,0.6306697408336693,0.48122579065652676,0.638298810279238,0.49632746998950145,0.6802586922298651,0.44064091408090805,0.573451719991905
|
47 |
+
3,10,0.4348210488874449,0.6522854375961137,0.4766802803438675,0.6637290417644666,0.4904023903603795,0.7018743889923095,0.43180204368077524,0.5709086968433822
|
48 |
+
3,11,0.4276015960825411,0.6522854375961137,0.4717793882186167,0.6637290417644666,0.48543618715307224,0.6815302038041265,0.4271390540599405,0.5709086968433822
|
49 |
+
3,-1,0.4276015960825411,0.6522854375961137,0.4717793882186167,0.6637290417644666,0.48543618715307224,0.6815302038041265,0.4271390540599405,0.5709086968433822
|
50 |
+
4,1,0.4214239914175457,0.6522854375961137,0.46865185069274773,0.6433848565762836,0.48193724414031425,0.6815302038041265,0.42254227189527593,0.5899813704573036
|
51 |
+
4,2,0.4191327427060846,0.6522854375961137,0.46799568205581066,0.6306697408336693,0.4811730082862524,0.6815302038041265,0.4209502267898128,0.5899813704573036
|
52 |
+
4,3,0.4177497228755946,0.6522854375961137,0.4672704266606563,0.6306697408336693,0.48046145518131045,0.662457530190205,0.4205336189755251,0.5899813704573036
|
53 |
+
4,4,0.41278138052242225,0.6522854375961137,0.4641914001937418,0.6306697408336693,0.47692992354815494,0.662457530190205,0.417234061877104,0.5899813704573036
|
54 |
+
4,5,0.4084090262303802,0.6522854375961137,0.4621233524420026,0.6306697408336693,0.47432753029063485,0.662457530190205,0.41376405626271445,0.5899813704573036
|
55 |
+
4,6,0.4047743004972669,0.6522854375961137,0.4601638008541454,0.6522854375961137,0.47226688906536857,0.662457530190205,0.410876474462412,0.5899813704573036
|
56 |
+
4,7,0.40290509826355414,0.6522854375961137,0.4595599937813398,0.6522854375961137,0.4716438963000161,0.662457530190205,0.4091652073540324,0.5899813704573036
|
57 |
+
4,8,0.4015618901718633,0.6522854375961137,0.45895194633326486,0.6522854375961137,0.4710175591054945,0.662457530190205,0.40796382884516,0.5899813704573036
|
58 |
+
4,9,0.4006955092468836,0.6522854375961137,0.458540064681458,0.6522854375961137,0.4706556692406096,0.662457530190205,0.40705849491130947,0.5899813704573036
|
59 |
+
4,10,0.40019220934913363,0.6522854375961137,0.4583861098107952,0.6522854375961137,0.47054695265617924,0.662457530190205,0.40648849636892403,0.5899813704573036
|
60 |
+
4,11,0.4002625503328767,0.6522854375961137,0.45847018403200823,0.6522854375961137,0.47069029268484636,0.662457530190205,0.40649131146535,0.5899813704573036
|
61 |
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4,-1,0.4002625503328767,0.6522854375961137,0.45847018403200823,0.6522854375961137,0.47069029268484636,0.662457530190205,0.40649131146535,0.5899813704573036
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modules.json
ADDED
@@ -0,0 +1,20 @@
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|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
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2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
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"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 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Dense",
|
18 |
+
"type": "sentence_transformers.models.Dense"
|
19 |
+
}
|
20 |
+
]
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:530201cc4bdbd3f0e72e57341b92d151831f198db6155835b138999f0bb81ee4
|
3 |
+
size 439484849
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 256,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
similarity_evaluation_sts-test_results.csv
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
epoch,steps,cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
|
2 |
+
-1,-1,0.8112052968349174,0.502398020492055,0.8124335366238343,0.5049980878771866,0.814252374975173,0.5090931940087687,0.7911350575329102,0.5122132748709267
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special_tokens_map.json
ADDED
@@ -0,0 +1 @@
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|
|
|
|
1 |
+
{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
|
tokenizer.json
ADDED
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|
tokenizer_config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": false, "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": "/root/.cache/huggingface/transformers/78141ed1e8dcc5ff370950397ca0d1c5c9da478f54ec14544187d8a93eff1a26.f982506b52498d4adb4bd491f593dc92b2ef6be61bfdbe9d30f53f963f9f5b66", "name_or_path": "dccuchile/bert-base-spanish-wwm-uncased", "tokenizer_class": "BertTokenizer"}
|
vocab.txt
ADDED
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|
|