Awesome

We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains. Experiments on two representative generative models, i.e., LLaMA and Flan-T5, show that the integration of negative instances into the training process yields substantial performance enhancements. The resulting models, GNER-LLaMA and GNER-T5, outperform state-of-the-art (SoTA) approaches by a large margin, achieving improvements of 8 and 11 points in $F_1$ score, respectively. Code and models are publicly available.

• 📖 Paper: Rethinking Negative Instances for Generative Named Entity Recognition
• 💾 Models in the 🤗 HuggingFace Hub: GNER-Models
• 🧪 Reproduction Materials: Materials
• 🎨 Example Jupyter Notebooks: GNER Notebooks

PreTrained Models

We release five GNER models based on LLaMA (7B) and Flan-T5 (base, large, xl and xxl).

Demo usage

Please check out Example Jupyter Notebooks for guidance on utilizing GNER models.

A simple inference example is as follows:

GNER-LLaMA:

>>> import torch
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B")
>>> model = AutoModelForCausalLM.from_pretrained("dyyyyyyyy/GNER-LLaMA-7B", torch_dtype=torch.bfloat16).cuda()
>>> model = model.eval()
>>> instruction_template = "Please analyze the sentence provided, identifying the type of entity for each word on a token-by-token basis.\nOutput format is: word_1(label_1), word_2(label_2), ...\nWe'll use the BIO-format to label the entities, where:\n1. B- (Begin) indicates the start of a named entity.\n2. I- (Inside) is used for words within a named entity but are not the first word.\n3. O (Outside) denotes words that are not part of a named entity.\n"
>>> sentence = "did george clooney make a musical in the 1980s"
>>> entity_labels = ["genre", "rating", "review", "plot", "song", "average ratings", "director", "character", "trailer", "year", "actor", "title"]
>>> instruction = f"{instruction_template}\nUse the specific entity tags: {', '.join(entity_labels)} and O.\nSentence: {sentence}"
>>> instruction = f"[INST] {instruction} [/INST]"
>>> inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
>>> outputs = model.generate(**inputs, max_new_tokens=640)
>>> response = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> response = response[response.find("[/INST]") + len("[/INST]"):].strip()
>>> print(response)
"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)"

GNER-T5:

>>> import torch
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> tokenizer = AutoTokenizer.from_pretrained("dyyyyyyyy/GNER-T5-xxl")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("dyyyyyyyy/GNER-T5-xxl", torch_dtype=torch.bfloat16).cuda()
>>> model = model.eval()
>>> instruction_template = "Please analyze the sentence provided, identifying the type of entity for each word on a token-by-token basis.\nOutput format is: word_1(label_1), word_2(label_2), ...\nWe'll use the BIO-format to label the entities, where:\n1. B- (Begin) indicates the start of a named entity.\n2. I- (Inside) is used for words within a named entity but are not the first word.\n3. O (Outside) denotes words that are not part of a named entity.\n"
>>> sentence = "did george clooney make a musical in the 1980s"
>>> entity_labels = ["genre", "rating", "review", "plot", "song", "average ratings", "director", "character", "trailer", "year", "actor", "title"]
>>> instruction = f"{instruction_template}\nUse the specific entity tags: {', '.join(entity_labels)} and O.\nSentence: {sentence}"
>>> inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
>>> outputs = model.generate(**inputs, max_new_tokens=640)
>>> response = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> print(response)
"did(O) george(B-actor) clooney(I-actor) make(O) a(O) musical(B-genre) in(O) the(O) 1980s(B-year)"

Task schema: Incorporating negative instances into training

Hierarchical Matching: A faster algorithm for structuring

We develop a Hierarchical Matching algorithm that provides a straightforward and effective solution to the omission, addition, and substitution problems in the structuring process.

Furthermore, we implement a fast version of the LCS algorithm within $O(N\log N)$, based on the nature of the small number of duplicate words in the query sentence.

First, we transform the Longest Common Subsequence (LCS) problem into a Longest Increasing Subsequence (LIS) problem. Subsequently, we construct a Directed Acyclic Graph (DAG) to facilitate the traceback of the specific sequence.

# A fast version of LCS with a complexity of O(NlogN)
# in the condiction that there are few depulicate words in the sentence
# input: a = [word_1, word_2, ..., word_n], b = [word_1, word_2, ..., word_m]
# return: match_idx = [idx_1, idx_2, ..., idx_n] (correspoding matching index between a and b)
def lcs_solve_fast(a, b):
    n, m = len(a), len(b)
    match_idx = [-1] * n
    match_list_b = defaultdict(list)
  
    # First we can convert the LCS problem into a LIS problem,
    # i.e., LCS(a, b) <=> LIS(index_list)
    for idx, word in enumerate(reversed(b)):
        match_list_b[word].append(m - idx - 1)
    index_list = []
    elem_list = []
    for idx, word in enumerate(a):
        if word in match_list_b:
            index_list.extend(match_list_b[word])
            elem_list.extend([idx] * len(match_list_b[word]))

    # then we compute the longest increasing subsequence of index_list
    # we compute a dag, the edges array store the parent of the node, and path store the results
    father, increasing_seq = [[(-1, -1, -1)]], [-1]
    for i in range(len(index_list)):
        if index_list[i] > increasing_seq[-1]:
            father.append([(len(father[-1]) - 1, i, index_list[i])])
            increasing_seq.append(index_list[i])
        else:
            # binary search
            l, r, query_idx = 0, len(increasing_seq) - 1, -1
            while l <= r:
                mid = (l + r) >> 1
                if increasing_seq[mid] >= index_list[i]:
                    query_idx = mid
                    r = mid - 1
                else:
                    l = mid + 1
            father[query_idx].append((len(father[query_idx - 1]) - 1, i, index_list[i]))
            increasing_seq[query_idx] = index_list[i]

    # finally, we trace back the path to get a solution of the original LCS problem
    i, j = len(father) - 1, len(father[-1]) - 1
    while i > 0:
        match_idx[elem_list[father[i][j][1]]] = father[i][j][2]
        j = father[i][j][0]
        i -= 1
    return match_idx

How to use GNER

Requirements

You should install the dependencies:

# CUDA 11.7 and above
# PyTorch 2.0 and above.
# transformers>=4.32.0,<4.38.0
pip install torch datasets deepspeed accelerate transformers protobuf

Quick Reproduction

We also provide all the generated results for quick reproduction of our results. The model_predictions folder contains the generated results of GNER-LLaMA-7B and GNER-T5-xxl (including the ground truth). You can execute the following commands to evaluate the generated results:

# 0shot performance of GNER-LLaMA
python evaluate.py --tokenizer-path yahma/llama-7b-hf --prediction-path prediction_results/llama-7b-task-adaptation-beam1.jsonl
# 0shot performance of GNER-T5-xxl
python evaluate.py --tokenizer-path google/flan-t5-xxl --prediction-path prediction_results/flan-t5-xxl-task-adaptation-beam1.jsonl

Other generated results can be found at here, and the execution process is similar to the two examples mentioned above.

Training & Inference

First, you should download the training data from here, put it in the current directory and rename it as data

The training scripts are outlined in folder scripts, you can train and evaluate the model by the following command:

# Train and evaluate LLaMA Model
bash scripts/train_llama_task_adaptation.sh
# Evaluate only
bash scripts/eval_llama_task_adaptation.sh

# Train T5 xxl Model
bash scripts/train_t5_xxl_task_adaptation.sh
# Evaluate only
bash scripts/eval_t5_task_adaptation.sh

Citation

@misc{ding2024rethinking,
      title={Rethinking Negative Instances for Generative Named Entity Recognition}, 
      author={Yuyang Ding and Juntao Li and Pinzheng Wang and Zecheng Tang and Bowen Yan and Min Zhang},
      year={2024},
      eprint={2402.16602},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}