Abhi964

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,76 @@
+---
+library_name: transformers
+license: mit
+base_model: ai4bharat/indic-bert
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: Paraphrase_indicBERT_onfull_FT3
+  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. -->
+
+# Paraphrase_indicBERT_onfull_FT3
+
+This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0320
+- Accuracy: 0.789
+- F1: 0.7885
+
+## 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: 1.4638638566821256e-05
+- train_batch_size: 32
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 14
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.5707        | 1.0   | 157  | 0.5842          | 0.6895   | 0.6630 |
+| 0.4831        | 2.0   | 314  | 0.5444          | 0.7435   | 0.7420 |
+| 0.4363        | 3.0   | 471  | 0.4700          | 0.775    | 0.7730 |
+| 0.3548        | 4.0   | 628  | 0.4781          | 0.7765   | 0.7763 |
+| 0.2468        | 5.0   | 785  | 0.5416          | 0.786    | 0.7858 |
+| 0.2046        | 6.0   | 942  | 0.6293          | 0.7775   | 0.7768 |
+| 0.127         | 7.0   | 1099 | 0.6558          | 0.7815   | 0.7802 |
+| 0.1042        | 8.0   | 1256 | 0.9524          | 0.742    | 0.7381 |
+| 0.0653        | 9.0   | 1413 | 1.0619          | 0.7485   | 0.7450 |
+| 0.0253        | 10.0  | 1570 | 1.0320          | 0.789    | 0.7885 |
+| 0.0405        | 11.0  | 1727 | 1.1028          | 0.7795   | 0.7794 |
+| 0.0106        | 12.0  | 1884 | 1.1150          | 0.784    | 0.7840 |
+| 0.0098        | 13.0  | 2041 | 1.1362          | 0.785    | 0.7850 |
+| 0.0331        | 14.0  | 2198 | 1.1453          | 0.785    | 0.7850 |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

config.json

@@ -0,0 +1,34 @@
+{
+  "_name_or_path": "ai4bharat/indic-bert",
+  "architectures": [
+    "AlbertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "gap_size": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "inner_group_num": 1,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "albert",
+  "net_structure_type": 0,
+  "num_attention_heads": 12,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 12,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "type_vocab_size": 2,
+  "vocab_size": 200000
+}

model.safetensors

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special_tokens_map.json

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+}

spiece.model

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tokenizer.json

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tokenizer_config.json

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+  "do_lower_case": true,
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+  "sp_model_kwargs": {},
+  "tokenizer_class": "AlbertTokenizer",
+  "unk_token": "<unk>"
+}

training_args.bin

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