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Open LLM Leaderboard: DROP deep dive | clefourrier | December 1, 2023 | leaderboard-drop-dive | community, research, nlp, evaluation, leaderboard | https://huggingface.co/blog/leaderboard-drop-dive | Diving into the results Extending our investigations, our friends at Zeno joined us and undertook a much more thorough exploration of the results, looking at 5 models which were representative of the problems we noticed in DROP scores: falcon-180B and mistral-7B were underperforming compared to what we were expecting, Yi-34B and tigerbot-70B had a very good performance on DROP correlated with their average scores, and facebook/xglm-7.5B fell in the middle. You can give analyzing the results a try in the Zeno project here if you want to! The Zeno team found two even more concerning features: 1) Not a single model got a correct result on floating point answers 2) High quality models which generate long answers actually have a lower f1-score At this point, we believed that both failure cases were actually caused by the same root factor: using . as a stopword token (to end the generations): 1) Floating point answers are systematically interrupted before their generation is complete 2) Higher quality models, which try to match the few-shot prompt format, will generate Answer\n\nPlausible prompt for the next question., and only stop during the plausible prompt continuation after the actual answer on the first ., therefore generating too many words and getting a bad f1 score. We hypothesized that both these problems could be fixed by using \n instead of . as an end of generation stop word. | [
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Open LLM Leaderboard: DROP deep dive | clefourrier | December 1, 2023 | leaderboard-drop-dive | community, research, nlp, evaluation, leaderboard | https://huggingface.co/blog/leaderboard-drop-dive | # Open LLM Leaderboard: DROP deep dive Recently, three new benchmarks were added to the Open LLM Leaderboard: Winogrande, GSM8k and DROP, using the original implementations reproduced in the EleutherAI Harness. A cursory look at the scores for DROP revealed something strange was going on, with the overwhelming majority of models scoring less than 10 out of 100 on their f1-score! We did a deep dive to understand what was going on, come with us to see what we found out! | [
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Open LLM Leaderboard: DROP deep dive | clefourrier | December 1, 2023 | leaderboard-drop-dive | community, research, nlp, evaluation, leaderboard | https://huggingface.co/blog/leaderboard-drop-dive | Normalization interrogations During our first deeper dive in these surprising behavior, we observed that the normalization step was possibly not working as intended: in some cases, this normalization ignored the correct numerical answers when they were directly followed by a whitespace character other than a space (a line return, for example). Let's look at an example, with the generation being 10\n\nPassage: The 2011 census recorded a population of 1,001,360, and the gold answer being 10. Normalization happens in several steps, both for generation and gold: 1) Split on separators |, -, or The beginning sequence of the generation 10\n\nPassage: contain no such separator, and is therefore considered a single entity after this step. 2) Punctuation removal The first token then becomes 10\n\nPassage (: is removed) 3) Homogenization of numbers Every string that can be cast to float is considered a number and cast to float, then re-converted to string. 10\n\nPassage stays the same, as it cannot be cast to float, whereas the gold 10 becomes 10.0. 4) Other steps A lot of other normalization steps ensue (removing articles, removing other whitespaces, etc.) and our original example becomes 10 passage 2011.0 census recorded population of 1001360.0. However, the overall score is not computed on the string, but on the bag of words (BOW) extracted from the string, here {'recorded', 'population', 'passage', 'census', '2011.0', '1001360.0', '10'}, which is compared with the BOW of the gold, also normalized in the above manner, {10.0}. As you can see, they don’t intersect, even though the model predicted the correct output! In summary, if a number is followed by any kind of whitespace other than a simple space, it will not pass through the number normalization, hence never match the gold if it is also a number! This first issue was likely to mess up the scores quite a bit, but clearly it was not the only factor causing DROP scores to be so low. We decided to investigate a bit more. | [
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Open LLM Leaderboard: DROP deep dive | clefourrier | December 1, 2023 | leaderboard-drop-dive | community, research, nlp, evaluation, leaderboard | https://huggingface.co/blog/leaderboard-drop-dive | Initial observations DROP (Discrete Reasoning Over Paragraphs) is an evaluation where models must extract relevant information from English-text paragraphs before executing discrete reasoning steps on them (for example, sorting or counting items to arrive at the correct answer, see the table below for examples). The metrics used are custom f1 and exact match scores. Examples of reasoning and paragraph from the original article. We added it to the Open LLM Leaderboard three weeks ago, and observed that the f1-scores of pretrained models followed an unexpected trend: when we plotted DROP scores against the leaderboard original average (of ARC, HellaSwag, TruthfulQA and MMLU), which is a reasonable proxy for overall model performance, we expected DROP scores to be correlated with it (with better models having better performance). However, this was only the case for a small number of models, and all the others had a very low DROP f1-score, below 10. Two trends can be observed in the DROP scores: some follow the average (in diagonal), others are stuck around 5 (vertical line on the right of the graph). | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | LoRA LoRA is a fine-tuning technique that belongs to the family of "parameter-efficient" (PEFT) methods, which try to reduce the number of trainable parameters affected by the fine-tuning process. It increases fine-tuning speed while reducing the size of fine-tuned checkpoints. Instead of fine-tuning the model by performing tiny changes to all its weights, we freeze most of the layers and only train a few specific ones in the attention blocks. Furthermore, we avoid touching the parameters of those layers by adding the product of two smaller matrices to the original weights. Those small matrices are the ones whose weights are updated during the fine-tuning process, and then saved to disk. This means that all of the model original parameters are preserved, and we can load the LoRA weights on top using an adaptation method. The LoRA name (Low Rank Adaptation) comes from the small matrices we mentioned. For more information about the method, please refer to this post or the original paper. The diagram above shows two smaller orange matrices that are saved as part of the LoRA adapter. We can later load the LoRA adapter and merge it with the blue base model to obtain the yellow fine-tuned model. Crucially, unloading the adapter is also possible so we can revert back to the original base model at any point. In other words, the LoRA adapter is like an add-on of a base model that can be added and removed on demand. And because of A and B smaller ranks, it is very light in comparison with the model size. Therefore, loading is much faster than loading the whole base model. If you look, for example, inside the Stable Diffusion XL Base 1.0 model repo, which is widely used as a base model for many LoRA adapters, you can see that its size is around 7 GB. However, typical LoRA adapters like this one take a mere 24 MB of space ! There are far less blue base models than there are yellow ones on the Hub. If we can go quickly from the blue to yellow one and vice versa, then we have a way serve many distinct yellow models with only a few distinct blue deployments. For a more exhaustive presentation on what LoRA is, please refer to the following blog post:Using LoRA for Efficient Stable Diffusion Fine-Tuning, or refer directly to the original paper. | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | Implementation We implemented LoRA mutualization in the Inference API. When a request is performed on a model available in our platform, we first determine whether this is a LoRA or not. We then identify the base model for the LoRA and route the request to a common backend farm, with the ability to serve requests for the said model. Inference requests get served by keeping the base model warm and loading/unloading LoRAs on the fly. This way we can ultimately reuse the same compute resources to serve many distinct models at once. | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | Loading/Offloading LoRA for Diffusers 🧨 Note that there is a more seemless way to perform the same as what is presented in this section using the peft library. Please refer to the documentation for more details. The principle remains the same as below (going from/to the blue box to/from the yellow one in the diagram above) 4 functions are used in the Diffusers library to load and unload distinct LoRA weights: load_lora_weights and fuse_lora for loading and merging weights with the main layers. Note that merging weights with the main model before performing inference can decrease the inference time by 30%. unload_lora_weights and unfuse_lora for unloading. We provide an example below on how one can leverage the Diffusers library to quickly load several LoRA weights on top of a base model: py import torch from diffusers import ( AutoencoderKL, DiffusionPipeline, ) import time base = "stabilityai/stable-diffusion-xl-base-1.0" adapter1 = 'nerijs/pixel-art-xl' weightname1 = 'pixel-art-xl.safetensors' adapter2 = 'minimaxir/sdxl-wrong-lora' weightname2 = None inputs = "elephant" kwargs = {} if torch.cuda.is_available(): kwargs["torch_dtype"] = torch.float16 start = time.time() # Load VAE compatible with fp16 created by madebyollin vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, ) kwargs["vae"] = vae kwargs["variant"] = "fp16" model = DiffusionPipeline.from_pretrained( base, **kwargs ) if torch.cuda.is_available(): model.to("cuda") elapsed = time.time() - start print(f"Base model loaded, elapsed {elapsed:.2f} seconds") def inference(adapter, weightname): start = time.time() model.load_lora_weights(adapter, weight_name=weightname) # Fusing lora weights with the main layers improves inference time by 30 % ! model.fuse_lora() elapsed = time.time() - start print(f"LoRA adapter loaded and fused to main model, elapsed {elapsed:.2f} seconds") start = time.time() data = model(inputs, num_inference_steps=25).images[0] elapsed = time.time() - start print(f"Inference time, elapsed {elapsed:.2f} seconds") start = time.time() model.unfuse_lora() model.unload_lora_weights() elapsed = time.time() - start print(f"LoRA adapter unfused/unloaded from base model, elapsed {elapsed:.2f} seconds") inference(adapter1, weightname1) inference(adapter2, weightname2) | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | Loading figures All numbers below are in seconds: GPU T4 A10G Base model loading - not cached 20 20 Base model loading - cached 5.95 4.09 Adapter 1 loading 3.07 3.46 Adapter 1 unloading 0.52 0.28 Adapter 2 loading 1.44 2.71 Adapter 2 unloading 0.19 0.13 Inference time 20.7 8.5 With 2 to 4 additional seconds per inference, we can serve many distinct LoRAs. However, on an A10G GPU, the inference time decreases by a lot while the adapters loading time does not change much, so the LoRA's loading/unloading is relatively more expensive. | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | # Goodbye cold boot - how we made LoRA Inference 300% faster tl;dr: We swap the Stable Diffusion LoRA adapters per user request, while keeping the base model warm allowing fast LoRA inference across multiple users. You can experience this by browsing our LoRA catalogue and playing with the inference widget. In this blog we will go in detail over how we achieved that. We've been able to drastically speed up inference in the Hub for public LoRAs based on public Diffusion models. This has allowed us to save compute resources and provide a faster and better user experience. To perform inference on a given model, there are two steps: 1. Warm up phase - that consists in downloading the model and setting up the service (25s). 2. Then the inference job itself (10s). With the improvements, we were able to reduce the warm up time from 25s to 3s. We are now able to serve inference for hundreds of distinct LoRAs, with less than 5 A10G GPUs, while the response time to user requests decreased from 35s to 13s. Let's talk more about how we can leverage some recent features developed in the Diffusers library to serve many distinct LoRAs in a dynamic fashion with one single service. | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | Benefits We have approximately 2500 distinct public LoRAs on the Hub. The vast majority (~92%) of them are LoRAs based on the Stable Diffusion XL Base 1.0 model. Before this mutualization, this would have meant deploying a dedicated service for all of them (eg. for all the yellow merged matrices in the diagram above); releasing + reserving at least one new GPU. The time to spawn the service and have it ready to serve requests for a specific model is approximately 25s, then on top of this you have the inference time (~10s for a 1024x1024 SDXL inference diffusion with 25 inference steps on an A10G). If an adapter is only occasionally requested, its service gets stopped to free resources preempted by others. If you were requesting a LoRA that was not so popular, even if it was based on the SDXL model like the vast majority of adapters found on the Hub so far, it would have required 35s to warm it up and get an answer on the first request (the following ones would have taken the inference time, eg. 10s). Now: request time has decreased from 35s to 13s since adapters will use only a few distinct "blue" base models (like 2 significant ones for Diffusion). Even if your adapter is not so popular, there is a good chance that its "blue" service is already warmed up. In other words, there is a good chance that you avoid the 25s warm up time, even if you do not request your model that often. The blue model is already downloaded and ready, all we have to do is unload the previous adapter and load the new one, which takes 3s as we see below. Overall, this requires less GPUs to serve all distinct models, even though we already had a way to share GPUs between deployments to maximize their compute usage. In a 2min time frame, there are approximately 10 distinct LoRA weights that are requested. Instead of spawning 10 deployments, and keeping them warm, we simply serve all of them with 1 to 2 GPUs (or more if there is a request burst). | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | What about batching ? Recently a really interesting paper came out, that described how to increase the throughput by performing batched inference on LoRA models. In short, all inference requests would be gathered in a batch, the computation related to the common base model would be done all at once, then the remaining adapter-specific products would be computed. We did not implement such a technique (close to the approach adopted in text-generation-inference for LLMs). Instead, we stuck to single sequential inference requests. The reason is that we observed that batching was not interesting for diffusers: throughput does not increase significantly with batch size. On the simple image generation benchmark we performed, it only increased 25% for a batch size of 8, in exchange for 6 times increased latency! Comparatively, batching is far more interesting for LLMs because you get 8 times the sequential throughput with only a 10% latency increase. This is the reason why we did not implement batching for diffusers. | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | Conclusion: Time! Using dynamic LoRA loading, we were able to save compute resources and improve the user experience in the Hub Inference API. Despite the extra time added by the process of unloading the previously loaded adapter and loading the one we're interested in, the fact that the serving process is most often already up and running makes the inference time response on the whole much shorter. Note that for a LoRA to benefit from this inference optimization on the Hub, it must both be public, non-gated and based on a non-gated public model. Please do let us know if you apply the same method to your deployment! | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | Serving requests To serve inference requests, we use this open source community image You can find the previously described mechanism used in the TextToImagePipeline class. When a LoRA is requested, we'll look at the one that is loaded and change it only if required, then we perform inference as usual. This way, we are able to serve requests for the base model and many distinct adapters. Below is an example on how you can test and request this image: $ git clone https://github.com/huggingface/api-inference-community.git $ cd api-inference-community/docker_images/diffusers $ docker build -t test:1.0 -f Dockerfile . $ cat > /tmp/env_file <<'EOF' MODEL_ID=stabilityai/stable-diffusion-xl-base-1.0 TASK=text-to-image HF_HUB_ENABLE_HF_TRANSFER=1 EOF $ docker run --gpus all --rm --name test1 --env-file /tmp/env_file_minimal -p 8888:80 -it test:1.0 Then in another terminal perform requests to the base model and/or miscellaneous LoRA adapters to be found on the HF Hub. # Request the base model $ curl 0:8888 -d '{"inputs": "elephant", "parameters": {"num_inference_steps": 20}}' > /tmp/base.jpg # Request one adapter $ curl -H 'lora: minimaxir/sdxl-wrong-lora' 0:8888 -d '{"inputs": "elephant", "parameters": {"num_inference_steps": 20}}' > /tmp/adapter1.jpg # Request another one $ curl -H 'lora: nerijs/pixel-art-xl' 0:8888 -d '{"inputs": "elephant", "parameters": {"num_inference_steps": 20}}' > /tmp/adapter2.jpg | [
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Goodbye cold boot - how we made LoRA inference 300% faster | raphael-gl | December 5, 2023 | lora-adapters-dynamic-loading | diffusers, lora, models, inference, stable-diffusion | https://huggingface.co/blog/lora-adapters-dynamic-loading | LoRA structure In the Hub, LoRAs can be identified with two attributes: A LoRA will have a base_model attribute. This is simply the model which the LoRA was built for and should be applied to when performing inference. Because LoRAs are not the only models with such an attribute (any duplicated model will have one), a LoRA will also need a lora tag to be properly identified. | [
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Optimum-NVIDIA - Unlock blazingly fast LLM inference in just 1 line of code | laikh-nvidia | December 5, 2023 | optimum-nvidia | llm, nvidia, llama, inference, optimum | https://huggingface.co/blog/optimum-nvidia | # Optimum-NVIDIA on Hugging Face enables blazingly fast LLM inference in just 1 line of code Large Language Models (LLMs) have revolutionized natural language processing and are increasingly deployed to solve complex problems at scale. Achieving optimal performance with these models is notoriously challenging due to their unique and intense computational demands. Optimized performance of LLMs is incredibly valuable for end users looking for a snappy and responsive experience, as well as for scaled deployments where improved throughput translates to dollars saved. That's where the Optimum-NVIDIA inference library comes in. Available on Hugging Face, Optimum-NVIDIA dramatically accelerates LLM inference on the NVIDIA platform through an extremely simple API. By changing just a single line of code, you can unlock up to 28x faster inference and 1,200 tokens/second on the NVIDIA platform. Optimum-NVIDIA is the first Hugging Face inference library to benefit from the new float8 format supported on the NVIDIA Ada Lovelace and Hopper architectures. FP8, in addition to the advanced compilation capabilities of NVIDIA TensorRT-LLM software software, dramatically accelerates LLM inference. | [
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Optimum-NVIDIA - Unlock blazingly fast LLM inference in just 1 line of code | laikh-nvidia | December 5, 2023 | optimum-nvidia | llm, nvidia, llama, inference, optimum | https://huggingface.co/blog/optimum-nvidia | Performance Evaluation When evaluating the performance of an LLM, we consider two metrics: First Token Latency and Throughput. First Token Latency (also known as Time to First Token or prefill latency) measures how long you wait from the time you enter your prompt to the time you begin receiving your output, so this metric can tell you how responsive the model will feel. Optimum-NVIDIA delivers up to 3.3x faster First Token Latency compared to stock transformers: Figure 1. Time it takes to generate the first token (ms) Throughput, on the other hand, measures how fast the model can generate tokens and is particularly relevant when you want to batch generations together. While there are a few ways to calculate throughput, we adopted a standard method to divide the end-to-end latency by the total sequence length, including both input and output tokens summed over all batches. Optimum-NVIDIA delivers up to 28x better throughput compared to stock transformers: Figure 2. Throughput (token / second) Initial evaluations of the recently announced NVIDIA H200 Tensor Core GPU show up to an additional 2x boost in throughput for LLaMA models compared to an NVIDIA H100 Tensor Core GPU. As H200 GPUs become more readily available, we will share performance data for Optimum-NVIDIA running on them. | [
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Optimum-NVIDIA - Unlock blazingly fast LLM inference in just 1 line of code | laikh-nvidia | December 5, 2023 | optimum-nvidia | llm, nvidia, llama, inference, optimum | https://huggingface.co/blog/optimum-nvidia | Next steps Optimum-NVIDIA currently provides peak performance for the LLaMAForCausalLM architecture + task, so any LLaMA-based model, including fine-tuned versions, should work with Optimum-NVIDIA out of the box today. We are actively expanding support to include other text generation model architectures and tasks, all from within Hugging Face. We continue to push the boundaries of performance and plan to incorporate cutting-edge optimization techniques like In-Flight Batching to improve throughput when streaming prompts and INT4 quantization to run even bigger models on a single GPU. Give it a try: we are releasing the Optimum-NVIDIA repository with instructions on how to get started. Please share your feedback with us! 🤗 | [
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Optimum-NVIDIA - Unlock blazingly fast LLM inference in just 1 line of code | laikh-nvidia | December 5, 2023 | optimum-nvidia | llm, nvidia, llama, inference, optimum | https://huggingface.co/blog/optimum-nvidia | How to Run You can start running LLaMA with blazingly fast inference speeds in just 3 lines of code with a pipeline from Optimum-NVIDIA. If you already set up a pipeline from Hugging Face’s transformers library to run LLaMA, you just need to modify a single line of code to unlock peak performance! diff - from transformers.pipelines import pipeline + from optimum.nvidia.pipelines import pipeline # everything else is the same as in transformers! pipe = pipeline('text-generation', 'meta-llama/Llama-2-7b-chat-hf', use_fp8=True) pipe("Describe a real-world application of AI in sustainable energy.") You can also enable FP8 quantization with a single flag, which allows you to run a bigger model on a single GPU at faster speeds and without sacrificing accuracy. The flag shown in this example uses a predefined calibration strategy by default, though you can provide your own calibration dataset and customized tokenization to tailor the quantization to your use case. The pipeline interface is great for getting up and running quickly, but power users who want fine-grained control over setting sampling parameters can use the Model API. diff - from transformers import AutoModelForCausalLM + from optimum.nvidia import AutoModelForCausalLM from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf", padding_side="left") model = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-13b-chat-hf", + use_fp8=True, ) model_inputs = tokenizer( ["How is autonomous vehicle technology transforming the future of transportation and urban planning?"], return_tensors="pt" ).to("cuda") generated_ids, generated_length = model.generate( **model_inputs, top_k=40, top_p=0.7, repetition_penalty=10, ) tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True) For more details, check out our documentation | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | Training 1. Aspect candidate extraction In this work we assume that aspects, which are usually features of products and services, are mostly nouns or noun compounds (strings of consecutive nouns). We use spaCy to tokenize and extract nouns/noun compounds from the sentences in the (few-shot) training set. Since not all extracted nouns/noun compounds are aspects, we refer to them as aspect candidates. 2. Aspect/Non-aspect classification Now that we have aspect candidates, we need to train a model to be able to distinguish between nouns that are aspects and nouns that are non-aspects. For this purpose, we need training samples with aspect/no-aspect labels. This is done by considering aspects in the training set as True aspects, while other non-overlapping candidate aspects are considered non-aspects and therefore labeled as False: * Training sentence: "Waiters aren't friendly but the cream pasta is out of this world." * Tokenized: [Waiters, are, n't, friendly, but, the, cream, pasta, is, out, of, this, world, .] * Extracted aspect candidates: [Waiters, are, n't, friendly, but, the, cream, pasta, is, out, of, this, world, .] * Gold labels from training set, in BIO format: [B-ASP, O, O, O, O, O, B-ASP, I-ASP, O, O, O, O, O, .] * Generated aspect/non-aspect Labels: [Waiters, are, n't, friendly, but, the, cream, pasta, is, out, of, this, world, .] Now that we have all the aspect candidates labeled, how do we use it to train the candidate aspect classification model? In other words, how do we use SetFit, a sentence classification framework, to classify individual tokens? Well, this is the trick: each aspect candidate is concatenated with the entire training sentence to create a training instance using the following template: aspect_candidate:training_sentence Applying the template to the example above will generate 3 training instances – two with True labels representing aspect training instances, and one with False label representing non-aspect training instance: | Text | Label | |:------------------------------------------------------------------------------|:------| | Waiters:Waiters aren't friendly but the cream pasta is out of this world. | 1 | | cream pasta:Waiters aren't friendly but the cream pasta is out of this world. | 1 | | world:Waiters aren't friendly but the cream pasta is out of this world. | 0 | | ... | ... | After generating the training instances, we are ready to use the power of SetFit to train a few-shot domain-specific binary classifier to extract aspects from an input text review. This will be our first fine-tuned SetFit model. 3. Sentiment polarity classification Once the system extracts the aspects from the text, it needs to associate a sentiment polarity (e.g., positive, negative or neutral) to each aspect. For this purpose, we use a 2nd SetFit model and train it in a similar fashion to the aspect extraction model as illustrated in the following example: * Training sentence: "Waiters aren't friendly but the cream pasta is out of this world." * Tokenized: [Waiters, are, n't, friendly, but, the, cream, pasta, is, out, of, this, world, .] * Gold labels from training set: [NEG, O, O, O, O, O, POS, POS, O, O, O, O, O, .] | Text | Label | |:------------------------------------------------------------------------------|:------| | Waiters:Waiters aren't friendly but the cream pasta is out of this world. | NEG | | cream pasta:Waiters aren't friendly but the cream pasta is out of this world. | POS | | ... | ... | Note that as opposed to the aspect extraction model, we don't include non-aspects in this training set because the goal is to classify the sentiment polarity towards real aspects. | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | How does it work? SetFitABSA's three-stage training process SetFitABSA is comprised of three steps. The first step extracts aspect candidates from the text, the second one yields the aspects by classifying the aspect candidates as aspects or non-aspects, and the final step associates a sentiment polarity to each extracted aspect. Steps two and three are based on SetFit models. | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | # SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit SetFitABSA is an efficient technique to detect the sentiment towards specific aspects within the text. Aspect-Based Sentiment Analysis (ABSA) is the task of detecting the sentiment towards specific aspects within the text. For example, in the sentence, "This phone has a great screen, but its battery is too small", the aspect terms are "screen" and "battery" and the sentiment polarities towards them are Positive and Negative, respectively. ABSA is widely used by organizations for extracting valuable insights by analyzing customer feedback towards aspects of products or services in various domains. However, labeling training data for ABSA is a tedious task because of the fine-grained nature (token level) of manually identifying aspects within the training samples. Intel Labs and Hugging Face are excited to introduce SetFitABSA, a framework for few-shot training of domain-specific ABSA models; SetFitABSA is competitive and even outperforms generative models such as Llama2 and T5 in few-shot scenarios. Compared to LLM based methods, SetFitABSA has two unique advantages: 🗣 No prompts needed: few-shot in-context learning with LLMs requires handcrafted prompts which make the results brittle, sensitive to phrasing and dependent on user expertise. SetFitABSA dispenses with prompts altogether by generating rich embeddings directly from a small number of labeled text examples. 🏎 Fast to train: SetFitABSA requires only a handful of labeled training samples; in addition, it uses a simple training data format, eliminating the need for specialized tagging tools. This makes the data labeling process fast and easy. In this blog post, we'll explain how SetFitABSA works and how to train your very own models using the SetFit library. Let's dive in! | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | Benchmarking SetFitABSA was benchmarked against the recent state-of-the-art work by AWS AI Labs and Salesforce AI Research that finetune T5 and GPT2 using prompts. To get a more complete picture, we also compare our model to the Llama-2-chat model using in-context learning. We use the popular Laptop14 and Restaurant14 ABSA datasets from the Semantic Evaluation Challenge 2014 (SemEval14). SetFitABSA is evaluated both on the intermediate task of aspect term extraction (SB1) and on the full ABSA task of aspect extraction along with their sentiment polarity predictions (SB1+SB2). | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | Performance comparison We see a clear advantage of SetFitABSA when the number of training instances is low, despite being 2x smaller than T5 and x3 smaller than GPT2-medium. Even when compared to Llama 2, which is x64 larger, the performance is on par or better. SetFitABSA vs GPT2 SetFitABSA vs T5 Note that for fair comparison, we conducted comparisons with SetFitABSA against exactly the dataset splits used by the various baselines (GPT2, T5, etc.). SetFitABSA vs Llama2 We notice that increasing the number of in-context training samples for Llama2 did not result in improved performance. This phenomenon has been shown for ChatGPT before, and we think it should be further investigated. | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | Training your own model SetFitABSA is part of the SetFit framework. To train an ABSA model, start by installing setfit with the absa option enabled: shell python -m pip install -U "setfit[absa]" Additionally, we must install the en_core_web_lg spaCy model: shell python -m spacy download en_core_web_lg We continue by preparing the training set. The format of the training set is a Dataset with the columns text, span, label, ordinal: * text: The full sentence or text containing the aspects. * span: An aspect from the full sentence. Can be multiple words. For example: "food". * label: The (polarity) label corresponding to the aspect span. For example: "positive". The label names can be chosen arbitrarily when tagging the collected training data. * ordinal: If the aspect span occurs multiple times in the text, then this ordinal represents the index of those occurrences. Often this is just 0, as each aspect usually appears only once in the input text. For example, the training text "Restaurant with wonderful food but worst service I ever seen" contains two aspects, so will add two lines to the training set table: | Text | Span | Label | Ordinal | |:-------------------------------------------------------------|:--------|:---------|:--------| | Restaurant with wonderful food but worst service I ever seen | food | positive | 0 | | Restaurant with wonderful food but worst service I ever seen | service | negative | 0 | | ... | ... | ... | ... | Once we have the training dataset ready we can create an ABSA trainer and execute the training. SetFit models are fairly efficient to train, but as SetFitABSA involves two models trained sequentially, it is recommended to use a GPU for training to keep the training time low. For example, the following training script trains a full SetFitABSA model in about 10 minutes with the free Google Colab T4 GPU. python from datasets import load_dataset from setfit import AbsaTrainer, AbsaModel # Create a training dataset as above # For convenience we will use an already prepared dataset here train_dataset = load_dataset("tomaarsen/setfit-absa-semeval-restaurants", split="train[:128]") # Create a model with a chosen sentence transformer from the Hub model = AbsaModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2") # Create a trainer: trainer = AbsaTrainer(model, train_dataset=train_dataset) # Execute training: trainer.train() That's it! We have trained a domain-specific ABSA model. We can save our trained model to disk or upload it to the Hugging Face hub. Bear in mind that the model contains two submodels, so each is given its own path: python model.save_pretrained( "models/setfit-absa-model-aspect", "models/setfit-absa-model-polarity" ) # or model.push_to_hub( "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect", "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity" ) Now we can use our trained model for inference. We start by loading the model: python from setfit import AbsaModel model = AbsaModel.from_pretrained( "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-aspect", "tomaarsen/setfit-absa-paraphrase-mpnet-base-v2-restaurants-polarity" ) Then, we use the predict API to run inference. The input is a list of strings, each representing a textual review: python preds = model.predict([ "Best pizza outside of Italy and really tasty.", "The food variations are great and the prices are absolutely fair.", "Unfortunately, you have to expect some waiting time and get a note with a waiting number if it should be very full." ]) print(preds) # [ # [{'span': 'pizza', 'polarity': 'positive'}], # [{'span': 'food variations', 'polarity': 'positive'}, {'span': 'prices', 'polarity': 'positive'}], # [{'span': 'waiting time', 'polarity': 'neutral'}, {'span': 'waiting number', 'polarity': 'neutral'}] # ] For more details on training options, saving and loading models, and inference see the SetFit docs. | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | Model size comparison | Model | Size (params) | |:------------------:|:-------------:| | Llama-2-chat | 7B | | T5-base | 220M | | GPT2-base | 124M | | GPT2-medium | 355M | | SetFit (MPNet) | 2x 110M | Note that for the SB1 task, SetFitABSA is 110M parameters, for SB2 it is 110M parameters, and for SB1+SB2 SetFitABSA consists of 220M parameters. | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | Running inference At inference time, the test sentence passes through the spaCy aspect candidate extraction phase, resulting in test instances using the template aspect_candidate:test_sentence. Next, non-aspects are filtered by the aspect/non-aspect classifier. Finally, the extracted aspects are fed to the sentiment polarity classifier that predicts the sentiment polarity per aspect. In practice, this means the model can receive normal text as input, and output aspects and their sentiments: Model Input: "their dinner specials are fantastic." Model Output: [{'span': 'dinner specials', 'polarity': 'positive'}] | [
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SetFitABSA: Few-Shot Aspect Based Sentiment Analysis using SetFit | ronenlap | December 6, 2023 | setfit-absa | research, nlp | https://huggingface.co/blog/setfit-absa | References * Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014. SemEval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 27–35. * Siddharth Varia, Shuai Wang, Kishaloy Halder, Robert Vacareanu, Miguel Ballesteros, Yassine Benajiba, Neha Anna John, Rishita Anubhai, Smaranda Muresan, Dan Roth, 2023 "Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis". https://arxiv.org/abs/2210.06629 * Ehsan Hosseini-Asl, Wenhao Liu, Caiming Xiong, 2022. "A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis". https://arxiv.org/abs/2204.05356 * Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, Oren Pereg, 2022. "Efficient Few-Shot Learning Without Prompts". https://arxiv.org/abs/2209.11055 | [
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