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README.md

@@ -1,3 +1,94 @@
----
-license: mit
----
+---
+license: mit
+language:
+- ru
+metrics:
+- f1
+- roc_auc
+- precision
+- recall
+pipeline_tag: text-classification
+tags:
+- sentiment-analysis
+- multi-class-classification
+- sentiment analysis
+- rubert
+- sentiment
+- bert
+- tiny
+- russian
+- multiclass
+- classification
+datasets:
+- sismetanin/rureviews
+- RuSentiment
+- LinisCrowd2015
+- LinisCrowd2016
+- KaggleRussianNews
+---
+
+This is [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for __sentiment classification__ of short __Russian__ texts.
+The task is a __multi-class classification__ with the following labels:
+
+```yaml
+0: neutral
+1: positive
+2: negative
+```
+
+Label to Russian label:
+
+```yaml
+neutral: нейтральный
+positive: позитивный
+negative: негативный
+```
+
+## Usage
+
+```python
+from transformers import pipeline
+model = pipeline(model="seara/rubert-tiny2-russian-sentiment")
+model("Привет, ты мне нравишься!")
+# [{'label': 'positive', 'score': 0.9398769736289978}]
+```
+
+## Dataset
+
+This model was trained on the union of the following datasets:
+
+- Kaggle Russian News Dataset
+- Linis Crowd 2015
+- Linis Crowd 2016
+- RuReviews
+- RuSentiment
+
+An overview of the training data can be found on [S. Smetanin Github repository](https://github.com/sismetanin/sentiment-analysis-in-russian).
+
+__Download links for all Russian sentiment datasets collected by Smetanin can be found in this [repository](https://github.com/searayeah/russian-sentiment-emotion-datasets).__
+
+## Training
+
+Training were done in this [project](https://github.com/searayeah/bert-russian-sentiment-emotion) with this parameters:
+
+```yaml
+tokenizer.max_length: 512
+batch_size: 64
+optimizer: adam
+lr: 0.00001
+weight_decay: 0
+epochs: 5
+```
+
+Train/validation/test splits are 80%/10%/10%.
+
+## Eval results (on test split)
+
+
+|         |neutral|positive|negative|macro avg|weighted avg|
+|---------|-------|--------|--------|---------|------------|
+|precision|0.7    |0.84    |0.74    |0.76     |0.75        |
+|recall   |0.74   |0.83    |0.69    |0.75     |0.75        |
+|f1-score |0.72   |0.83    |0.71    |0.75     |0.75        |
+|auc-roc  |0.85   |0.95    |0.91    |0.9      |0.9         |
+|support  |5196   |3831    |3599    |12626    |12626       |

config.json

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+{
+  "_name_or_path": "cointegrated/rubert-tiny2",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "emb_size": 312,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 312,
+  "id2label": {
+    "0": "neutral",
+    "1": "positive",
+    "2": "negative"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 600,
+  "label2id": {
+    "negative": 2,
+    "neutral": 0,
+    "positive": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 2048,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 3,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.29.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 83828
+}

model.safetensors

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+size 116801868

pytorch_model.bin

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 2048,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}