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README.md

@@ -1,3 +1,35 @@
 ---
+language: en
 license: apache-2.0
+datasets:
+- sst2
+- glue
+metrics:
+- accuracy
+tags:
+- text-classfication
+- int8
 ---
+
+# Dynamically quantized DistilBERT base uncased finetuned SST-2
+
+## Table of Contents
+- [Model Details](#model-details)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+
+## Model Details
+**Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor).
+- **Model Type:** Text Classification
+- **Language(s):** English
+- **License:** Apache-2.0
+- **Parent Model:** For more details on the original model, we encourage users to check out [this](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model card.
+
+## How to Get Started With the Model
+
+To load the quantized model, you can do as follows:
+
+```python
+from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification
+
+model = IncQuantizedModelForSequenceClassification.from_pretrained("Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic")
+```

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased-finetuned-sst-2-english",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "finetuning_task": "sst-2",
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "NEGATIVE",
+    "1": "POSITIVE"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "NEGATIVE": 0,
+    "POSITIVE": 1
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_past": true,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "int8",
+  "transformers_version": "4.19.4",
+  "vocab_size": 30522
+}

eval_results.json

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+{
+    "eval_accuracy": 0.9071100917431193,
+    "eval_loss": 0.3806319534778595,
+    "eval_runtime": 26.6222,
+    "eval_samples_per_second": 32.755,
+    "eval_steps_per_second": 4.094
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:980e9ed4055e37d54ba12c52383eb6b41bf8a4c11dde676aebab0d80e8d16531
+size 139416577

quantization.yml

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+device: cpu
+model:
+  framework: pytorch
+  name: bert
+quantization:
+  approach: post_training_dynamic_quant
+tuning:
+  accuracy_criterion:
+    relative: 0.02
+  exit_policy:
+    max_trials: 4
+    timeout: 0
+  random_seed: 9527
+version: 1.0