初始化项目，由ModelHub XC社区提供模型

Model: ITESM/st_demo_6
Source: Original Platform

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1_Pooling/config.json

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+{
+  "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

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+language:
+- es
+datasets:
+- hackathon-pln-es/nli-es
+widget:
+- text: "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."
+- text: "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
+- text: "Tendremos que optar por hacer una huelga para cobrar lo que queremos."
+- text: "Queda descartada la huelga aunque no cobremos lo que queramos."
+---
+
+# bertin-roberta-base-finetuning-esnli
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained on a
+collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+Based around the siamese networks approach from [this paper](https://arxiv.org/pdf/1908.10084.pdf).
+<!--- Describe your model here -->
+
+You can see a demo for this model [here](https://huggingface.co/spaces/hackathon-pln-es/Sentence-Embedding-Bertin).
+
+You can find our other model, **paraphrase-spanish-distilroberta** [here](https://huggingface.co/hackathon-pln-es/paraphrase-spanish-distilroberta) and its demo [here](https://huggingface.co/spaces/hackathon-pln-es/Paraphrase-Bertin).
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["Este es un ejemplo", "Cada oración es transformada"]
+
+model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
+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('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
+model = AutoModel.from_pretrained('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
+
+# 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, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+Our model was evaluated on the task of Semantic Textual Similarity using the [SemEval-2015 Task](https://alt.qcri.org/semeval2015/task2/) for [Spanish](http://alt.qcri.org/semeval2015/task2/data/uploads/sts2015-es-test.zip). We measure 
+
+|                    | [BETO STS](https://huggingface.co/espejelomar/sentece-embeddings-BETO) | BERTIN STS (this model) | Relative improvement |
+|-------------------:|---------:|-----------:|---------------------:|
+|   cosine_pearson   | 0.609803 | 0.683188   | +12.03               |
+|   cosine_spearman  | 0.528776 | 0.615916   | +16.48               |
+|  euclidean_pearson | 0.590613 | 0.672601   | +13.88               |
+| euclidean_spearman | 0.526529 | 0.611539   | +16.15               |
+|  manhattan_pearson | 0.589108 | 0.672040   | +14.08               |
+| manhattan_spearman | 0.525910 | 0.610517   | +16.09               |
+|     dot_pearson    | 0.544078 | 0.600517   | +10.37               |
+|    dot_spearman    | 0.460427 | 0.521260   | +13.21               |
+
+
+## Training
+The model was trained with the parameters:
+
+**Dataset**
+
+We used a collection of datasets of Natural Language Inference as training data:
+ - [ESXNLI](https://raw.githubusercontent.com/artetxem/esxnli/master/esxnli.tsv), only the part in spanish
+ - [SNLI](https://nlp.stanford.edu/projects/snli/), automatically translated
+ - [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), automatically translated
+
+The whole dataset used is available [here](https://huggingface.co/datasets/hackathon-pln-es/nli-es).
+
+Here we leave the trick we used to increase the amount of data for training here:
+```
+  for row in reader:
+    if row['language'] == 'es':
+      
+      sent1 = row['sentence1'].strip()
+      sent2 = row['sentence2'].strip()
+    
+      add_to_samples(sent1, sent2, row['gold_label'])
+      add_to_samples(sent2, sent1, row['gold_label'])  #Also add the opposite
+```
+
+**DataLoader**:
+
+`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader`
+of length 1818 with parameters:
+```
+{'batch_size': 64}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 10,
+    "evaluation_steps": 0,
+    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 909,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (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})
+)
+```
+
+## Authors
+
+[Anibal Pérez](https://huggingface.co/Anarpego),
+
+[Emilio Tomás Ariza](https://huggingface.co/medardodt),
+
+[Lautaro Gesuelli](https://huggingface.co/Lgesuelli) y
+
+[Mauricio Mazuecos](https://huggingface.co/mmazuecos).

config.json

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+{
+  "_name_or_path": "./hackathon-pln-es_bertin-roberta-base-finetuning-esnli/",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "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": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.19.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.0",
+    "transformers": "4.17.0",
+    "pytorch": "1.10.2"
+  }
+}

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"
+  }
+]

pytorch_model.bin

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sentence_bert_config.json

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+{
+  "max_seq_length": 514,
+  "do_lower_case": false
+}

special_tokens_map.json

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

tokenizer_config.json

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+{"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": false, "trim_offsets": true, "special_tokens_map_file": null, "name_or_path": "./hackathon-pln-es_bertin-roberta-base-finetuning-esnli/", "tokenizer_class": "RobertaTokenizer"}