levshechter/tibetan-code-switching-detector

README.md

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+---
+library_name: transformers
+base_model: OMRIDRORI/mbert-tibetan-continual-unicode-240k
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: combined_model_4class
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# combined_model_4class
+
+This model is a fine-tuned version of [OMRIDRORI/mbert-tibetan-continual-unicode-240k](https://huggingface.co/OMRIDRORI/mbert-tibetan-continual-unicode-240k) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3774
+- Accuracy: 0.9519
+- Proximity F1: 0.0021
+- Proximity Recall: 0.0012
+- Proximity Precision: 0.0075
+- Exact Matches: 0.0075
+- Missed Switches: 0.0821
+- False Switches: 0.0896
+- Matches At 1 Words: 0.0
+- Matches At 2 Words: 0.0
+- Matches At 3 Words: 0.0
+- Matches At 4 Words: 0.0
+- Matches At 5 Words: 0.0
+- Matches At 6 Words: 0.0
+- Matches At 7 Words: 0.0
+- Matches At 8 Words: 0.0
+- Matches At 9 Words: 0.0
+- Matches At 10 Words: 0.0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 40
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Accuracy | Proximity F1 | Proximity Recall | Proximity Precision | Exact Matches | Missed Switches | False Switches | Matches At 1 Words | Matches At 2 Words | Matches At 3 Words | Matches At 4 Words | Matches At 5 Words | Matches At 6 Words | Matches At 7 Words | Matches At 8 Words | Matches At 9 Words | Matches At 10 Words |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:------------:|:----------------:|:-------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:-------------------:|
+| 1.1691        | 26.6667 | 200  | 0.3774          | 0.9519   | 0.0021       | 0.0012           | 0.0075              | 0.0075        | 0.0821          | 0.0896         | 0.0                | 0.0                | 0.0                | 0.0                | 0.0                | 0.0                | 0.0                | 0.0                | 0.0                | 0.0                 |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.4.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

config.json

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+{
+  "_name_or_path": "OMRIDRORI/mbert-tibetan-continual-unicode-240k",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "non_switch_auto",
+    "1": "non_switch_allo",
+    "2": "to_auto",
+    "3": "to_allo"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "non_switch_allo": 1,
+    "non_switch_auto": 0,
+    "to_allo": 3,
+    "to_auto": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

final_model/config.json

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+{
+  "_name_or_path": "OMRIDRORI/mbert-tibetan-continual-unicode-240k",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "non_switch_auto",
+    "1": "non_switch_allo",
+    "2": "to_auto",
+    "3": "to_allo"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "non_switch_allo": 1,
+    "non_switch_auto": 0,
+    "to_allo": 3,
+    "to_auto": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

final_model/model.safetensors

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final_model/special_tokens_map.json

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final_model/tokenizer_config.json

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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
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+}

final_model/training_args.bin

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model.safetensors

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special_tokens_map.json

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tokenizer_config.json

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