pahautelman
/

phi2-ner-v1

@@ -1,201 +1,135 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+tags:
+- autotrain
+- text-generation
+- transformers
+- named entity recognition
+widget:
+- text: 'I love AutoTrain because '
+license: mit
+datasets:
+- conll2012_ontonotesv5
+language:
+- en
 ---
+# Phi-2 model fine-tuned for named entity recognition task
+The model was fine-tuned using one quarter of the ConLL 2012 OntoNotes v5 dataset.
+- Dataset Source: [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5)
+- Subset Used: English_v12
+- Number of Examples: 87,265
+The prompts and expected outputs were constructed as described in [1].
+Example input:
+```md
+Instruct: I am an excelent linquist. The task is to label organization entities in the given sentence. Below are some examples
+Input: A spokesman for B. A. T said of the amended filings that,`` It would appear that nothing substantive has changed.
+Output: A spokesman for @@B. A. T## said of the amended filings that,`` It would appear that nothing substantive has changed.
+Input: Since NBC's interest in the Qintex bid for MGM / UA was disclosed, Mr. Wright has n't been available for comment.
+Output: Since @@NBC##'s interest in the @@Qintex## bid for @@MGM / UA## was disclosed, Mr. Wright has n't been available for comment.
+Input: You know news organizations demand total transparency whether you're General Motors or United States government /.
+Output: You know news organizations demand total transparency whether you're @@General Motors## or United States government /.
+Input: We respectfully invite you to watch a special edition of Across China.
+Output:
+```
+Expected output:
+```md
+We respectfully invite you to watch a special edition of @@Across China##.
+```
+This model is trained to recognize the named entity categories
+- person
+- nationalities or religious or political groups
+- facility
+- organization
+- geopolitical entity
+- location
+- product
+- date
+- time expression
+- percentage
+- monetary value
+- quantity
+- event
+- work of art
+- law/legal reference
+- language name
+# Model Trained Using AutoTrain
+This model was trained using **SFT** AutoTrain trainer. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+Hyperparameters:
+```json
+{
+    "model": "microsoft/phi-2",
+    "valid_split": null,
+    "add_eos_token": false,
+    "block_size": 1024,
+    "model_max_length": 1024,
+    "padding": "right",
+    "trainer": "sft",
+    "use_flash_attention_2": false,
+    "disable_gradient_checkpointing": false,
+    "evaluation_strategy": "epoch",
+    "save_total_limit": 1,
+    "save_strategy": "epoch",
+    "auto_find_batch_size": false,
+    "mixed_precision": "bf16",
+    "lr": 0.0002,
+    "epochs": 1,
+    "batch_size": 1,
+    "warmup_ratio": 0.1,
+    "gradient_accumulation": 4,
+    "optimizer": "adamw_torch",
+    "scheduler": "linear",
+    "weight_decay": 0.01,
+    "max_grad_norm": 1.0,
+    "seed": 42,
+    "apply_chat_template": false,
+    "quantization": "int4",
+    "target_modules": null,
+    "merge_adapter": false,
+    "peft": true,
+    "lora_r": 16,
+    "lora_alpha": 32,
+    "lora_dropout": 0.05,
+    "dpo_beta": 0.1,
+}
+```
+# Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_path = "pahautelman/phi2-ner-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path
+).eval()
+prompt = 'Label the person entities in the given sentence: Russian President Vladimir Putin is due to arrive in Havana a few hours from now to become the first post-Soviet leader to visit Cuba.'
+inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt')
+outputs = model.generate(
+    inputs.to(model.device),
+    max_new_tokens=9,
+    do_sample=False,
+)
+output = tokenizer.batch_decode(outputs)[0]
+# Model response: "Output: Russian President, Vladimir Putin"
+print(output)
+```
+# References:
+[1] Wang et al., GPT-NER: Named entity recognition via large language models 2023