license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP | bd2b0e6d0af58246216951a1fc2e27cd |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 1 | 8.4025 | 4.8565 | 0.4435 | 3.9735 | 4.415 | 19.0 | |... | 1fc48484accf419f8d8dc484c1f19583 |
apache-2.0 | ['speech'] | false | Wav2Vec2-XLSR-53 [Facebook's XLSR-Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on ... | f058dca54bf4db171c86507be6a5c4dc |
apache-2.0 | ['speech'] | false | Usage See [this notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb) for more information on how to fine-tune the model.  for **Closed Book Question Answering**. The model was pre-trained using T5's denoising objective on [C4](https://huggingface.co/datasets/c4), subsequently additionally pre-trained using [REALM](https://arxiv.org/pdf/2002.08909.p... | da4da9893eadbc690c6fbb63ab4c0d14 |
apache-2.0 | [] | false | Results on Web Questions - Test Set |Id | link | Exact Match | |---|---|---| |**T5-11b**|**https://huggingface.co/google/t5-11b-ssm-wq**|**44.7**| |T5-xxl|https://huggingface.co/google/t5-xxl-ssm-wq|43.5| | 7c41b0f7808b15b25b10660a27e2f12c |
apache-2.0 | [] | false | Usage The model can be used as follows for **closed book question answering**: ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer t5_qa_model = AutoModelForSeq2SeqLM.from_pretrained("google/t5-11b-ssm-wq") t5_tok = AutoTokenizer.from_pretrained("google/t5-11b-ssm-wq") input_ids = t5_tok("When ... | 85f3e22c4d34d69257d315ea4fa807b7 |
apache-2.0 | [] | false | Abstract It has recently been observed that neural language models trained on unstructured text can implicitly store and retrieve knowledge using natural language queries. In this short paper, we measure the practical utility of this approach by fine-tuning pre-trained models to answer questions without access to any... | bb6881a09c91266bd2666dd67e30ed82 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_s... | cac380aec17176a633fdcda7d786c63c |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-v2.2 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset. It achieves the following results on the evaluation set: - Loss: 0.3595 - Precision: 0.8823 - Recall: 0.8497 - F1: 0.8657 - Accuracy: 0.9427 | 49bea52a41d962ba6d8a5fad3ba943d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2726 | 1.0 | 3228 | 0.4504 | 0.7390 | 0.7287 | 0.7338 | 0.9107 | | 0.2057 | 2.0 |... | 823829fd7e12d60d71c502db0a71904b |
mit | ['nowcasting', 'forecasting', 'timeseries', 'remote-sensing'] | false | Model description 3d conv model, that takes in different data streams architecture is roughly 1. satellite image time series goes into many 3d convolution layers. 2. nwp time series goes into many 3d convolution layers. 3. Final convolutional layer goes to full connected layer. This i... | 88377fd9867d8433808cf58b625b4cc2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2270 - Accuracy: 0.924 - F1: 0.9241 | ce6e11ea56e24b2dc68015d27d946299 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8204 | 1.0 | 250 | 0.3160 | 0.9035 | 0.9008 | | 0.253 | 2.0 | 500 | 0.2270 | 0.924 | 0.9241 | | 0b5d75287a663bb0b203856a4c76087e |
mit | ['endpoints-template', 'optimum'] | false | Optimized and Quantized [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) with a custom handler.py This repository implements a `custom` handler for `question-answering` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optiumum](https://huggingface.co/docs/optimum/index)... | ecfb6f23197b097f943cd31847e7d652 |
mit | ['endpoints-template', 'optimum'] | false | expected Request payload ```json { "inputs": { "question": "As what is Philipp working?", "context": "Hello, my name is Philipp and I live in Nuremberg, Germany. Currently I am working as a Technical Lead at Hugging Face to democratize artificial intelligence through open source and open science.... | d920888b6e2501473b4f615a8e1df7b8 |
mit | ['endpoints-template', 'optimum'] | false | Run Request ```python import json from typing import List import requests as r import base64 ENDPOINT_URL = "" HF_TOKEN = "" def predict(question:str=None,context:str=None): payload = {"inputs": {"question": question, "context": context}} response = r.post( ENDPOINT_URL, headers={"Authorization": ... | 20e65e025b15470afb62b7be848812de |
mit | ['endpoints-template', 'optimum'] | false | 5-push-to-repository-and-create-inference-endpoint) Helpful links: * [Accelerate Transformers with Hugging Face Optimum](https://huggingface.co/blog/optimum-inference) * [Optimizing Transformers for GPUs with Optimum](https://www.philschmid.de/optimizing-transformers-with-optimum-gpu) * [Optimum Documentation](https:/... | 7c6c11f24812aaa31e35a165c0d6d2ab |
mit | ['endpoints-template', 'optimum'] | false | 0. Base line Performance ```python from transformers import pipeline qa = pipeline("question-answering",model="deepset/roberta-base-squad2") ``` Okay, let's test the performance (latency) with sequence length of 128. ```python context="Hello, my name is Philipp and I live in Nuremberg, Germany. Currently I am wo... | 336a00b009eca212b27b5319e9a09f13 |
mit | ['endpoints-template', 'optimum'] | false | Timed run for _ in range(50): start_time = perf_counter() _ = pipe(question=payload["inputs"]["question"], context=payload["inputs"]["context"]) latency = perf_counter() - start_time latencies.append(latency) | d52ccd8cdeaf2c156d6c59c57266bcbd |
mit | ['endpoints-template', 'optimum'] | false | Compute run statistics time_avg_ms = 1000 * np.mean(latencies) time_std_ms = 1000 * np.std(latencies) return f"Average latency (ms) - {time_avg_ms:.2f} +\- {time_std_ms:.2f}" print(f"Vanilla model {measure_latency(qa,payload)}") | aa177068d99c7a0768b407d42cf26614 |
mit | ['endpoints-template', 'optimum'] | false | 1. Convert model to ONNX ```python from optimum.onnxruntime import ORTModelForQuestionAnswering from transformers import AutoTokenizer from pathlib import Path model_id="deepset/roberta-base-squad2" onnx_path = Path(".") | 08b73e504ec48e45572b202e34bc0f0c |
mit | ['endpoints-template', 'optimum'] | false | 2. Optimize & quantize model with Optimum ```python from optimum.onnxruntime import ORTOptimizer, ORTQuantizer from optimum.onnxruntime.configuration import OptimizationConfig, AutoQuantizationConfig | ce652d6c19c7cd9272d2a04f631de130 |
mit | ['endpoints-template', 'optimum'] | false | create ORTQuantizer and define quantization configuration dynamic_quantizer = ORTQuantizer.from_pretrained(onnx_path, file_name="model_optimized.onnx") dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False) | a0677301539223568e71f6408771fbe7 |
mit | ['endpoints-template', 'optimum'] | false | 3. Create Custom Handler for Inference Endpoints ```python %%writefile handler.py from typing import Dict, List, Any from optimum.onnxruntime import ORTModelForQuestionAnswering from transformers import AutoTokenizer, pipeline class EndpointHandler(): def __init__(self, path=""): | 683ed4fa325c851dc6ee321140bac6e7 |
mit | ['endpoints-template', 'optimum'] | false | load the optimized model self.model = ORTModelForQuestionAnswering.from_pretrained(path, file_name="model_optimized_quantized.onnx") self.tokenizer = AutoTokenizer.from_pretrained(path) | e9926da537ab30a75ebc4c84f866f5e4 |
mit | ['endpoints-template', 'optimum'] | false | create pipeline self.pipeline = pipeline("question-answering", model=self.model, tokenizer=self.tokenizer) def __call__(self, data: Any) -> List[List[Dict[str, float]]]: """ Args: data (:obj:): includes the input data and the parameters for the inference. ... | b379426da7b4162be3bc7310a991a129 |
mit | ['endpoints-template', 'optimum'] | false | prepare sample payload context="Hello, my name is Philipp and I live in Nuremberg, Germany. Currently I am working as a Technical Lead at Hugging Face to democratize artificial intelligence through open source and open science. In the past I designed and implemented cloud-native machine learning architectures for fin-... | 6b640c47791b869ed09ea56bdcacdd0f |
mit | ['endpoints-template', 'optimum'] | false | Compute run statistics time_avg_ms = 1000 * np.mean(latencies) time_std_ms = 1000 * np.std(latencies) return f"Average latency (ms) - {time_avg_ms:.2f} +\- {time_std_ms:.2f}" print(f"Optimized & Quantized model {measure_latency(my_handler,payload)}") | a628253b562487780bf06d991d3fea96 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.01 | 623d039802f129b37d01f619f9a43b6f |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_vp-nl_s6 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your ... | e90c881eb6a0a248dd6cf65e70728d15 |
apache-2.0 | ['generated_from_keras_callback'] | false | hsohn3/cchs-bert-visit-uncased-wordlevel-block512-batch8-ep10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.9857 - Epoch: 9 | 305c523f111bf8d1f486e06ac4dc92d4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 4dbca421c2e8c37d97233470d6451213 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Epoch | |:----------:|:-----:| | 4.4277 | 0 | | 3.1148 | 1 | | 3.0454 | 2 | | 3.0227 | 3 | | 3.0048 | 4 | | 3.0080 | 5 | | 2.9920 | 6 | | 2.9963 | 7 | | 2.9892 | 8 | | 2.9857 | 9 | | f3d03514781bb27cec16355bf212aade |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8686 - Wer: 0.6263 | a4fca7f876b09b485a42fe043a7beedd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.0505 | 13.89 | 500 | 3.0760 | 1.0 | | 1.2748 | 27.78 | 1000 | 0.8686 | 0.6263 | | 5cb04eca2d05614292cf411b315ad6f2 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 | 6864cbe2e019739b2c8be9675e1c92ca |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ... | bbe67b85d398cebcb97a751d9dffa0a9 |
mit | ['fastai'] | false | Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on... | 1cd004c0135d64b3e0f9f19b2f69e245 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | bart-base-finetuned-summarization-cnn-ver2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.1715 | dacd2490128f684cfb5b3df973ff7a82 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 | c022382c68f9b809d45d7d26d06ef0f2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Wav2Vec2-Large-XLSR-53-Irish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Irish using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. | fdbdf4552c537a896ae6b76949d6ec2d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ga-IE", split="test[:2%]"). processor = Wav2Vec2Processor.from_p... | 5e92bff839363c02a47c8d9fbd214ad6 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speec... | c8be1d6faaff673a550929e000584233 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ga-IE", split="test") w... | a3ffefaa8ff17f9977a93c3c859a6b3a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_datas... | 506c5d255a49b14d02d2f43f83822b36 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.a... | 40a5d782c9cdcf27876c3eedc8296363 |
apache-2.0 | ['translation'] | false | ita-vie * source group: Italian * target group: Vietnamese * OPUS readme: [ita-vie](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-vie/README.md) * model: transformer-align * source language(s): ita * target language(s): vie * model: transformer-align * pre-processing: normalization + S... | 04146b598c51189b387865f2b2f4e20e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ita-vie - source_languages: ita - target_languages: vie - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-vie/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'vi'] - src_constituents: {'ita'} - tgt_const... | 981dbc6da930c685cfb7bd42a817e8bf |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | -converting-models-to-core-ml).<br> Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br> `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> `original` version is only compatible with CPU & GPU option... | 3544adbeb5d2ef70622fc9ac3d96ad02 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Stable Diffusion v1-5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion). T... | e23d8f87ee04ccf53c7ce08a709c7cb1 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Diffusers ```py from diffusers import StableDiffusionPipeline import torch model_id = "runwayml/stable-diffusion-v1-5" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] ... | 2c183a7effd368c672fedede142de06f |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Original GitHub Repository 1. Download the weights - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v... | 009764aab044b94eb0c9d45222629d20 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.... | 07f88f0afbed3a5cf3474269b5f95711 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating en... | 364aca98373a927bc5e0d992e1cf0e0e |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be gener... | 9eeeca5fd58c0bc99ea85d1362dc729c |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images fr... | 42cdeddd211502a9c6ed83a37726f7f2 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hi... | 7d739b9d61e1a66ee31dfb5f5366498d |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space ... | 1243041939d536cfed0252b3c4cc8615 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 PLMS s... | 5eb3402e4c35c2548ac51f8cb9a146ba |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted... | 8fe8d837e68710b3c11675192b2d3fe8 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conferenc... | c5c1e99cb709368c2b7422e4d398a0e6 |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_wavlm_s250 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 1... | c51ec97bcc8a94a652ed511a462918dc |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-movie-sentiment-model-9000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4040 - Accuracy: 0.9178 - F1: 0.9155 | 4ff5eceffeda5faf2601cb5cba083bd0 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_pretrain_rte This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-complete](https://huggingface.co/gokuls/mobilebert_add_pre-training-complete) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.5271 ... | eb8556c77be76a56cf2fd0ecc4338b1c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0 | 1.0 | 20 | nan | 0.5271 | | 0.0 | 2.0 | 40 | nan | 0.5271 | | 0.0 | 3.0 | 60 | nan | 0.... | efeb72c8b6bb68a150c4ed30fd090ac2 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3322 - Accuracy: 0.8533 - F1: 0.8562 | 44acee131f215f6754b514a75100973e |
mit | ['generated_from_trainer'] | false | camembert-base-squad-fr This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5182 | 1d96f362f5623ba1f730e88e4d917195 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 | 37f175313c8e1c83469f37f564a09d77 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7504 | 1.0 | 3581 | 1.6470 | | 1.4776 | 2.0 | 7162 | 1.5182 | | 275621cc6cd5b2c9f6257c28a8df54fa |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_gender_male-10_female-0_s626 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using ... | 298dfda3f19f24454854a446d26fca67 |
mit | [] | false | Description This model is based on the pre-trained [NB-BERT-large model](https://huggingface.co/NbAiLab/nb-bert-large?text=P%C3%A5+biblioteket+kan+du+l%C3%A5ne+en+%5BMASK%5D.). It is a model for sentiment analysis. | c8cdb3fa91775823386df28662420a3b |
mit | [] | false | Data for fine-tuning This model was fine-tuned on 1000 exemples from the [NoReC train dataset](https://github.com/ltgoslo/norec) that belonged to the screen category. The training lasted 3 epochs with a learning rate of 5e-5. The code used to create this model (and some additional models) can be found on [Github](htt... | 3db31f584fa3bbf534b197ee425bd4c2 |
cc-by-4.0 | [] | false | Readability benchmark (ES): mbert-es-sentences-3class This project is part of a series of models from the paper "A Benchmark for Neural Readability Assessment of Texts in Spanish". You can find more details about the project in our [GitHub](https://github.com/lmvasque/readability-es-benchmark). | 50aa7e724de02ab41dda8a0a7e0827f5 |
cc-by-4.0 | [] | false | Models Our models were fine-tuned in multiple settings, including readability assessment in 2-class (simple/complex) and 3-class (basic/intermediate/advanced) for sentences and paragraph datasets. You can find more details in our [paper](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share... | af6b9d8a063cb619e6f236e6e2ddac49 |
cc-by-4.0 | [] | false | classes | |-----------------------------------------------------------------------------------------------------------|----------------|:---------:| | [BERTIN (ES)](https://huggingface.co/lmvasque/readability-es-benchmark-bertin-es-paragraphs-2class) | paragraphs | 2 | | [BERTIN (ES)](https://huggingface... | 5f3936a350354c44580fe5b60e0b2d20 |
cc-by-4.0 | [] | false | Results These are our results for all the readability models in different settings. Please select your model based on the desired performance: | Granularity | Model | F1 Score (2-class) | Precision (2-class) | Recall (2-class) | F1 Score (3-class) | Precision (3-class) | Recall (3-class) | |------... | 1fbf32abf59f2beb0a76a0231be6940a |
cc-by-4.0 | [] | false | Citation If you use our results and scripts in your research, please cite our work: "[A Benchmark for Neural Readability Assessment of Texts in Spanish](https://drive.google.com/file/d/1KdwvqrjX8MWYRDGBKeHmiR1NCzDcVizo/view?usp=share_link)" (to be published) ``` @inproceedings{vasquez-rodriguez-etal-2022-benchmarki... | be6b14468d06ad42176eb210076809d8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2699 - Accuracy: 0.9458 | e6ae98c1666d175becba7a1fc1f89953 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 9 | f2ede0120c1287cccdb53926cb560181 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2203 | 1.0 | 318 | 3.1656 | 0.7532 | | 2.4201 | 2.0 | 636 | 1.5891 | 0.8558 | | 1.1961 | 3.0 | 954 | 0.8037 | 0.... | 055a5277a122fbc3aca89b8102b1898e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_sch... | a6af920ca8e73431bb8bff3445e30b4d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-50k This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.5640 - Wer: 1.0 | 555ecfff67b790adb1277225b850dd1f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 0c60aab13e6ba30d09419749f4c887e9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 10.7005 | 0.48 | 300 | 5.3021 | 1.0 | | 3.9938 | 0.96 | 600 | 3.4997 | 1.0 | | 3.591 | 1.44 | 900 | 3.5641 | 1.0 | | 3.6168 ... | 62b4e2b6e34b30a4e3cf253256e0c4ed |
apache-2.0 | ['vision', 'image-classification'] | false | ResNet-34 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been writt... | b35a77271270f554818eb2f8b19ea273 |
apache-2.0 | ['vision', 'image-classification'] | false | Model description ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has str... | f101f21ff4fdb43a5cc39228cc93e8e5 |
apache-2.0 | ['vision', 'image-classification'] | false | Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) to look for fine-tuned versions on a task that interests you. | aa4921a57a4623c018ac42f48dc4553c |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoFeatureExtractor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image =... | 10f0e79c59c8597164588290c205e050 |
apache-2.0 | ['vision', 'image-classification'] | false | model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet). | e906935913dd9ba36de6de226009b547 |
apache-2.0 | ['vision', 'image-classification'] | false | BibTeX entry and citation info ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year... | 3fa644f0971db201d0a6dddb9eeb128a |
apache-2.0 | ['vision', 'image-classification'] | false | densenet121-res224-rsna A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all ... | 500659c65c09d3e889c668a388496071 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of xray: Note: Each pretrained model has 18 outputs. The `all` model has every output trained. However, for the other weights some targets are not trained and will predict randomly becuase they do not exist in the training dataset. The only valid outputs ... | d80eee5f2d8ad9f7b327cd494c78731a |
apache-2.0 | ['vision', 'image-classification'] | false | Add color channel img = img[None, :, :] transform = torchvision.transforms.Compose([xrv.datasets.XRayCenterCrop()]) img = transform(img) with torch.no_grad(): img = torch.from_numpy(img).unsqueeze(0) preds = model(img).cpu() output = { k: float(v) for k, v in zip(xrv.datasets.default_pat... | 826e3cb03c30df212f84521d016cfbc1 |
apache-2.0 | ['vision', 'image-classification'] | false | Citation Primary TorchXRayVision paper: [https://arxiv.org/abs/2111.00595](https://arxiv.org/abs/2111.00595) ``` Joseph Paul Cohen, Joseph D. Viviano, Paul Bertin, Paul Morrison, Parsa Torabian, Matteo Guarrera, Matthew P Lungren, Akshay Chaudhari, Rupert Brooks, Mohammad Hashir, Hadrien Bertrand TorchXRayVision: A ... | d522f986021e9a07cb616d548f775e06 |
apache-2.0 | [] | false | Chinese MRC macbert-large * 使用大量中文MRC数据训练的macbert-large模型，详情可查看：https://github.com/basketballandlearn/MRC_Competition_Dureader * 此库发布的再训练模型，在 阅读理解/分类 等任务上均有大幅提高<br/> （已有多位小伙伴在Dureader-2021等多个比赛中取得**top5**的成绩😁） | 模型/数据集 | Dureader-2021 | tencentmedical | | --------------------------... | 0caf317c5e214c8f2602408502a08a73 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2264 - Accuracy: 0.9275 - F1: 0.9275 | d5521bae133ec7d44a5f22df139f9437 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8546 | 1.0 | 250 | 0.3415 | 0.902 | 0.8975 | | 0.2647 | 2.0 | 500 | 0.2264 | 0.9275 | 0.9275 | | 949cf718aa73100cc8615b7e3271546a |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'uk'] | false | Ukrainian STT model (with Language Model) 🇺🇦 Join Ukrainian Speech Recognition Community - https://t.me/speech_recognition_uk ⭐ See other Ukrainian models - https://github.com/egorsmkv/speech-recognition-uk - Have a look on an updated 300m model: https://huggingface.co/Yehor/wav2vec2-xls-r-300m-uk-with-small-lm -... | f4e1a9d1dce84f85d51f1dae85be9463 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'uk'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 20 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 180301b070048d8b734a224434a45b5f |
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