Upload folder using huggingface_hub

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 ctfidf_config.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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
+}

README.md

@@ -1,89 +1,163 @@
-
 ---
+language:
+- multilingual
+- ar
+- bg
+- ca
+- cs
+- da
+- de
+- el
+- en 
+- es 
+- et 
+- fa 
+- fi 
+- fr
+- gl 
+- gu
+- he 
+- hi
+- hr
+- hu 
+- hy
+- id
+- it
+- ja
+- ka
+- ko
+- ku
+- lt
+- lv
+- mk
+- mn
+- mr
+- ms
+- my
+- nb
+- nl
+- pl
+- pt
+- ro
+- ru
+- sk
+- sl 
+- sq 
+- sr 
+- sv
+- th
+- tr
+- uk
+- ur
+- vi
+language_bcp47:
+- fr-ca
+- pt-br 
+- zh-cn
+- zh-tw
+pipeline_tag: sentence-similarity
+license: apache-2.0
 tags:
-- bertopic
-library_name: bertopic
-pipeline_tag: text-classification
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
 ---
 
-# mdl_bertopic_globo
+# sentence-transformers/paraphrase-multilingual-mpnet-base-v2
+
+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.
 
-This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. 
-BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. 
 
-## Usage 
 
-To use this model, please install BERTopic:
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
-pip install -U bertopic
+pip install -U sentence-transformers
 ```
 
-You can use the model as follows:
+Then you can use the model like this:
 
 ```python
-from bertopic import BERTopic
-topic_model = BERTopic.load("strauss-oak/mdl_bertopic_globo")
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+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.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
+model = AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')
 
-topic_model.get_topic_info()
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, average pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Evaluation Results
+
+
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (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})
+)
 ```
 
-## Topic overview
-
-* Number of topics: 20
-* Number of training documents: 24433
-
-<details>
-  <summary>Click here for an overview of all topics.</summary>
-  
-  | Topic ID | Topic Keywords | Topic Frequency | Label | 
-|----------|----------------|-----------------|-------| 
-| -1 | disse - anos - presidente - ainda - pessoas | 13 | -1_disse_anos_presidente_ainda | 
-| 0 | dose - saude - covid - vacina - casos | 7995 | 0_dose_saude_covid_vacina | 
-| 1 | presidente - bolsonaro - governo - disse - lula | 3785 | 1_presidente_bolsonaro_governo_disse | 
-| 2 | policia - disse - local - policiais - anos | 3626 | 2_policia_disse_local_policiais | 
-| 3 | anos - musica - brasil - gente - vai | 2170 | 3_anos_musica_brasil_gente | 
-| 4 | ucrania - russia - guerra - putin - disse | 1616 | 4_ucrania_russia_guerra_putin | 
-| 5 | chuva - sul - feira - chuvas - energia | 690 | 5_chuva_sul_feira_chuvas | 
-| 6 | preco - precos - produto - petrobras - combustiveis | 619 | 6_preco_precos_produto_petrobras | 
-| 7 | hospital - anos - familia - paulo - disse | 617 | 7_hospital_anos_familia_paulo | 
-| 8 | inflacao - juros - alta - taxa - economia | 501 | 8_inflacao_juros_alta_taxa | 
-| 9 | dezenas - aposta - premio - probabilidade - caixa | 378 | 9_dezenas_aposta_premio_probabilidade | 
-| 10 | voo - aeronave - aviao - voos - aeroporto | 370 | 10_voo_aeronave_aviao_voos | 
-| 11 | internet - iphone - rede - facebook - usuarios | 360 | 11_internet_iphone_rede_facebook | 
-| 12 | valor - pagamento - declaracao - valores - imposto | 342 | 12_valor_pagamento_declaracao_valores | 
-| 13 | fase - manifestantes - servicos - funcionar - paulo | 336 | 13_fase_manifestantes_servicos_funcionar | 
-| 14 | desmatamento - amazonia - indigenas - emissoes - brasil | 333 | 14_desmatamento_amazonia_indigenas_emissoes | 
-| 15 | taliba - israel - afeganistao - hamas - gaza | 327 | 15_taliba_israel_afeganistao_hamas | 
-| 16 | trabalho - trimestre - milhoes - trabalhadores - pessoas | 167 | 16_trabalho_trimestre_milhoes_trabalhadores | 
-| 17 | pessoas - genero - trans - racismo - lgbtqia | 97 | 17_pessoas_genero_trans_racismo | 
-| 18 | licenca - paternidade - licenca paternidade - piangers - ovulos | 91 | 18_licenca_paternidade_licenca paternidade_piangers |
-  
-</details>
-
-## Training hyperparameters
-
-* calculate_probabilities: False
-* language: None
-* low_memory: False
-* min_topic_size: 10
-* n_gram_range: (1, 1)
-* nr_topics: 20
-* seed_topic_list: None
-* top_n_words: 10
-* verbose: False
-* zeroshot_min_similarity: 0.7
-* zeroshot_topic_list: None
-
-## Framework versions
-
-* Numpy: 1.23.5
-* HDBSCAN: 0.8.33
-* UMAP: 0.5.5
-* Pandas: 1.5.3
-* Scikit-Learn: 1.2.2
-* Sentence-transformers: 2.2.2
-* Transformers: 4.35.2
-* Numba: 0.58.1
-* Plotly: 5.15.0
-* Python: 3.10.12
+## Citing & Authors
+
+This model was trained by [sentence-transformers](https://www.sbert.net/). 
+        
+If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
+```bibtex 
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "http://arxiv.org/abs/1908.10084",
+}
+```

config.json

@@ -1,16 +1,29 @@
 {
-  "calculate_probabilities": false,
-  "language": null,
-  "low_memory": false,
-  "min_topic_size": 10,
-  "n_gram_range": [
-    1,
-    1
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/sentence-transformers_paraphrase-multilingual-mpnet-base-v2/",
+  "architectures": [
+    "XLMRobertaModel"
   ],
-  "nr_topics": 20,
-  "seed_topic_list": null,
-  "top_n_words": 10,
-  "verbose": false,
-  "zeroshot_min_similarity": 0.7,
-  "zeroshot_topic_list": null
-}
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

model.safetensors

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+size 1112197096

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.json

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+size 17083009

tokenizer_config.json

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+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}