First model version

Neural_Engine_INT8_IR/conf.yaml

@@ -0,0 +1,2299 @@
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+  operator:
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+      output:
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+      attr:
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+    output_data:
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+        #'188:0': {}
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Neural_Engine_INT8_IR/model.bin

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+size 35125504

README.md

@@ -1,3 +1,21 @@
 ---
 license: mit
 ---
+
+# Sparse BERT mini model (uncased)
+
+Finetuned model pruned to 1:4 structured sparsity.
+The model is a pruned version of the [BERT mini model](https://huggingface.co/prajjwal1/bert-mini).
+
+## Intended Use
+
+The model can be used for inference with sparsity optimisztion.
+For further details on the model and its usage, see our repo and our implementation available [here](https://github.com/intel-innersource/frameworks.ai.nlp-toolkit.intel-nlp-toolkit).
+We also upload the quanted int8 BERT mini sparse Neural Engine IR (acc 87.15) here, could be directly used by NLP Toolkit ref inference.
+
+## Evaluation Results
+We get the following results on the sst2 tasks development set:
+
+| Task | SST-2 (Acc) |
+|------|-------------|
+|      |     87.2    |

config.json

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+{
+  "_name_or_path": "Intel/bert-mini-sst2-distilled-sparse-90-1X4-block",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "sst2",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 256,
+  "id2label": {
+    "0": "0",
+    "1": "1"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "label2id": {
+    "0": 0,
+    "1": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.16.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "google/bert_uncased_L-4_H-256_A-4", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"}