license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
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: 0.2217 - Accuracy: 0.924 - F1: 0.9241 | 47158314224dc5724e1c6d7b1b915c58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8136 | 1.0 | 250 | 0.3140 | 0.902 | 0.8998 | | 0.2501 | 2.0 | 500 | 0.2217 | 0.924 | 0.9241 | | 53aa2d0dadbc54c52e85a66a9bfc7220 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | model_zu-en_updated This model is a fine-tuned version of [Helsinki-NLP/opus-mt-mul-en](https://huggingface.co/Helsinki-NLP/opus-mt-mul-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8306 - Bleu: 27.1218 | 78dd2a34b9744a3e1d77185b2862cbb7 |
mit | ['pytorch', 'deberta', 'deberta-v2', 'question-answering', 'question answering', 'squad'] | false | How to use 使い方 transformersおよびpytorch、sentencepiece、Juman++をインストールしてください。 以下のコードを実行することで、Question-Answeringタスクを解かせることができます。 please execute this code. ```python import torch from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-base-japanese... | 42f1030bec2fc8d7be08ac527804ead8 |
creativeml-openrail-m | ['text-to-image', 'v2.0', 'Embedding'] | false | Textual Inversion embedding trained on 768x768 images from 80s box arts of Transformers and GIJoe toys and identical sources. *Install by downloading the embedding, and put it in the **\embeddings** folder.* ![01254-273003803-futuristic big game hunter sitting for a photo next to his large alien creature, proud, fea... | e1a7374cab27b20c2c76cc075b7c026a |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-BASE-DM256 (Deep-Narrow version) T5-Efficient-BASE-DM256 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | cb6754c086ad84fc31d7af9b70986966 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-base-dm256** - is of model type **Base** with the following variations: - **dm** is **256** It has **74.33** million parameters and thus requires *ca.* **297.32 MB** of memory in full precision (*fp32*) or **148.66 MB** of memory in half precision (... | ab308973305254161196e427cc45f1f0 |
mit | [] | false | Base model: [gpt2-large](https://huggingface.co/gpt2-large) Fine-tuned to generate responses on a dataset of [Vaccine public health tweets](https://github.com/TheRensselaerIDEA/generative-response-modeling). For more information about the dataset, task and training, see [our paper](https://arxiv.org/abs/2204.04353). ... | afa5428faa9895e15dcb2838b4a0a6ae |
apache-2.0 | ['generated_from_trainer'] | false | model_output_en_de This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1298 - Bleu: 33.9121 - Gen Len: 76.8132 | 0af65a56a06fb68b1d317c769c1c162f |
apache-2.0 | ['generated_from_trainer'] | false | BERT-tiny-sst2 This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4422 - Accuracy: 0.8372 | 65b75d970296b996d4f676dd3fad848b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3914 | 1.0 | 4210 | 0.4383 | 0.8211 | | 0.2577 | 2.0 | 8420 | 0.4422 | 0.8372 | | 0.212 | 3.0 | 12630 | 0.5460 ... | a19c2d1102694a61bc029118857b701d |
mit | ['generated_from_trainer'] | false | Klassifizierung-Gewerke This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0398 - F1: 0.9931 | c5da901eb8daf6f26fddd17952e0ba59 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1473 | 1.0 | 726 | 0.0952 | 0.9822 | | 0.0252 | 2.0 | 1452 | 0.0488 | 0.9918 | | 0.028 | 3.0 | 2178 | 0.0398 | 0.9931 | ... | 274821a0328101b96168f85afdc874da |
apache-2.0 | ['translation'] | false | opus-mt-sv-fj * source languages: sv * target languages: fj * OPUS readme: [sv-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-21.zip](https://... | 4e680be3f78f35206897e7ae3c43a1c8 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-es_s859 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | fbd63ebdf4a12c7f76e0c29e25429d25 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-mse-summarization This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1108 - Rouge1: 43.1145 - Rouge2: 23.2262 - Rougel: 37.218 - Rougelsum: 41.0897 - Bleurt: -0.8051 - Gen Len: 18.549 | fbea9429d19330bad5d1fe0f6c842cef |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | aa2375286368ba00dcec6d02ac12f34e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleurt | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:| | 1.5207 | 1.0 | 267 | 1.2922 | 38.8738 | 19.1958 | 32.8... | e2f108e7c0da53b4462b4f5fae8e792f |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-small-dequad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-gene... | 97f34663faaec3d03a18acc8f4829a4c |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt... | 7311d1cdfa8c107ff9cbb6b0c0715bf2 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-dequad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | | Score | Type | Dataset | |:-----... | 5025dc1f89a11c7149f98e24c2a9996f |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_dequad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 11 - ... | 623b7862507b9a23029a2bfa91b9620e |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-am This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4128 - Precision: 0.0054 - Recall: 0.0166 - F1: 0.0082 - Accuracy: 0.8423 | 2863eb155ef3e0cab9ae1fcc5bf71c3a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 167 | 0.4448 | 0.0 | 0.0 | 0.0 | 0.8573 | | No log | 2.0 |... | 9269fb7e6c8f4221c455c8b326989697 |
mit | ['pytorch', 'diffusers', 'unconditional-audio-generation', 'diffusion-models-class'] | false | Usage ```python from IPython.display import Audio from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("juancopi81/test-audio-diffusion-electronic") output = pipe() display(output.images[0]) display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) ``` | 594df111954ee1bdb84e9dd8b7823961 |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-large-subjqa-vanilla-movies-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: movies) via [`lmqg`](https://gith... | 1a12b4ac1538836964be7fc59121c5bc |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (movies) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asa... | a30cdb289de77ab3814f750b564d7435 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-large-subjqa... | 4d83d031af67cc14518c48a9d4228935 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-large-subjqa-vanilla-movies-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | | Score | Type | Dataset ... | 4256baf9fc42df316ceceb9d32f2fdce |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: movies - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 3 ... | a1547047ce80af7d45a871f57a7d9086 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | 8528-diffusion final 8528-diffusion is a latent text-to-image diffusion model, conditioned by fine-tuning to colorful character images. 8528 Diffusion is a fine-tuning model of Stable Diffusion v1.4 with AI output images (t2i and t2i with i2i). I recommend entering "low quality,worst quality," for Negative prompt an... | 6409fe6f1c7b20bfe97c2031e98644c6 |
creativeml-openrail-m | ['coreml', 'stable-diffusion', 'text-to-image'] | false | 8528-diffusion v0.2 8528-diffusion is a latent text-to-image diffusion model, conditioned by fine-tuning to colorful character images. 8528 Diffusion v0.2 & v0.1 is a fine-tuning model of Waifu Diffusion with AI output images (t2i and t2i with i2i). <img src=https://i.imgur.com/z4sFctp.png > | c12676419e52b353cce78a4b9e28b8c7 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Vietnamese This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 vi dataset. It achieves the following results on the evaluation set: - Loss: 0.7136 - Wer: 15.4925 | 8ca08263b83f4048cfea637b269f5fc5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0001 | 124.0 | 1000 | 0.7136 | 15.4925 | | 0.0001 | 249.0 | 2000 | 0.8532 | 17.0045 | | 0.0 | 374.0 | 3000 | 0.9251 | 19.097... | 0465026972255f8225fa59ea27cf8c10 |
apache-2.0 | [] | false | doc2query/msmarco-t5-small-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 querie... | 410b5ce4abd92e9d9566b612ab7e2df5 |
apache-2.0 | [] | false | Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/msmarco-t5-small-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
text = "Python is an interpreted, high-level and general-purpose pro... | 108215d1c02af2aa39714a64765c5bbe |
apache-2.0 | [] | false | Training
This model fine-tuned [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) for 31k training steps (about 4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 320 word pieces. Output text w... | 30eceefcce2ed485c73f30fc25b9a508 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Low Poly Game Building on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Game Building concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_game_building** | b3a63514180e44b3543558d6e164bb8d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model:  | cc3461c5bae6580c066bd41befb65bca |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/guutv) | a751f82bda4f79651b38ae7c94b9215e |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  but with different random seeds, which causes variation... | 2772b4b476e9ca95b76982b610d00fd1 |
apache-2.0 | ['multiberts', 'multiberts-seed_5'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_5') model = TFBertModel.from_pretrained("google/multiberts-seed_5... | 30261360b70f2e773275f9ed2108ebc2 |
apache-2.0 | ['generated_from_trainer', 'nlu', 'text-classification'] | false | bert-base-uncased-amazon-massive-intent This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on [Amazon Massive](https://huggingface.co/datasets/AmazonScience/massive) dataset (only en-US subset). It achieves the following results on the evaluation set: - Loss: 0.4897 -... | c70740bba27ab944f7c485fd24ca0f48 |
apache-2.0 | ['generated_from_trainer', 'nlu', 'text-classification'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 2.5862 | 1.0 | 720 | 1.0160 | 0.8096 | 0.8096 | | 1.0591 | 2.0 | 1440 | 0.6003 | 0.8716 | 0.8716 | | 0.4151 |... | 67e79bd428d046c66567e8205c142a7f |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Model Description Megatron-GPT 5B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 5B refers to the total trainable parameter count (5 Billion) [1, 2]. This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/use... | 23d4ee8d797da32bc8a199de0485a9bb |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Step 1: Install NeMo and dependencies You will need to install NVIDIA Apex and NeMo. ``` git clone https://github.com/ericharper/apex.git cd apex git checkout nm_v1.11.0 pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_n... | 8b9e8e4e688f74bc6008e51d58e550a4 |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Step 2: Launch eval server **Note.** The example below launches a model variant with Tensor Parallelism (TP) of 2 and Pipeline Parallelism (PP) of 1 on two GPUs. ``` git clone https://github.com/NVIDIA/NeMo.git cd NeMo/examples/nlp/language_modeling git checkout v1.11.0 python megatron_gpt_eval.py gpt_model_file=... | 81b5899625e438410cb3a624aeca22a3 |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Step 3: Send prompts to your model! ```python import json import requests port_num = 5555 headers = {"Content-Type": "application/json"} def request_data(data): resp = requests.put('http://localhost:{}/generate'.format(port_num), data=json.dumps(data), headers=head... | 7fd32a4e18a79a36f08c668d8d410693 |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | Evaluation results *Zero-shot performance.* Evaluated using [LM Evaluation Test Suite from AI21](https://github.com/AI21Labs/lm-evaluation) | ARC-Challenge | ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA | | ------------- | -------- | ----------- | --------- | ---------- | --- | -... | 6b46fc6341f89602521278e57712703d |
cc-by-4.0 | ['text generation', 'pytorch', 'causal-lm'] | false | References [1] [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) [2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.... | 30a79298da106c4a84c69a297f463344 |
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: 0.2265 - Accuracy: 0.9235 - F1: 0.9237 | e8589d2c3091b7ea78f1153bc7804025 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8243 | 1.0 | 250 | 0.3199 | 0.906 | 0.9025 | | 0.2484 | 2.0 | 500 | 0.2265 | 0.9235 | 0.9237 | | 930f18f192922b557751dedbd345765d |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-ROBERTaBECAS This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 2.5760 | ca2d481f3aac728cecaf94a6261cf845 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 11 - eval_batch_size: 11 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 9b3f02df78059e14c0874fd705a312f0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 4.3366 | | No log | 2.0 | 12 | 3.1395 | | No log | 3.0 | 18 | 2.6092 | | No log | 4.0 | 24 | 2.5084 ... | d9943dc4e76506b5a61a747eae538cb9 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sv', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.2779 - Wer: 0.2525 | 429b33b2b53e73d3a124234afe7594b3 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sv', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.3224 | 1.37 | 500 | 3.3354 | 1.0 | | 2.9318 | 2.74 | 1000 | 2.9361 | 1.0000 | | 2.1371 | 4.11 | 1500 | 1.1157 | 0.835... | 21121459e3c8737181dce7e58e412fc0 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'sv', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python eval.py --model_id patrickvonplaten/xls-r-300m-sv-cv8 --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash pytho... | 1c7b81433c8f15273fa4c4d41e683468 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | ko_core_news_sm Korean pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner. | Feature | Description | | --- | --- | | **Name** | `ko_core_news_sm` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`,... | 6c840c7b8b5b3de712b5fde1d260fbaf |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (2028 labels for 4 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `_SP`, `ecs`, `etm`, `f`, `f+f+jcj`, `f+f+jcs`, `f+f+jct`, `f+f+jxt`, `f+jca`, `f+jca+jp+ecc`, `f+jca+jp+ep+ef`, `f+jca+jxc`, `f+jca+jxc+jcm`, `f+jca+jxt`, `f+jcj`, `f+jcm... | b0526e4e0d7483c7c1c90282db0fce46 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 100.00 | | `TOKEN_P` | 100.00 | | `TOKEN_R` | 100.00 | | `TOKEN_F` | 100.00 | | `TAG_ACC` | 73.06 | | `POS_ACC` | 85.82 | | `SENTS_P` | 99.90 | | `SENTS_R` | 99.95 | | `SENTS_F` | 99.93 | | `DEP_UAS` | 73.61 | | `DEP_LAS` | 65.59 | | `LEMMA_ACC` | 83.57 | | `ENT... | 6af992164bb6c9280f22f33b58cde8ed |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-squad-plain_text-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.3247 | f85a29d410c1e814854732b54d767e78 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.5181 | 0.4 | 500 | 7.5716 | | 6.4657 | 0.8 | 1000 | 7.5778 | | 6.2336 | 1.2 | 1500 | 7.4653 | | 6.0699 | 1.6 | 2000 | 7.4193 ... | 86f006d4673f556cbfcd16d32c950ec5 |
apache-2.0 | ['generated_from_trainer'] | false | whisper-dpv-finetuned-WITH-AUGMENTATION-LOWER-LR This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5717 - Wer: 34.5241 | 6407580f937d1347dcf3e5e68d72435c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | b022967b47cf3a5a39edf4b6693000f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6221 | 0.62 | 1000 | 0.5345 | 35.9711 | | 0.4318 | 1.25 | 2000 | 0.5271 | 34.9537 | | 0.3859 | 1.87 | 3000 | 0.5338 | 34.365... | edfced2838ef8fe976e794754601d664 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation'] | false | IT5 Small for Question Generation 💭 🇮🇹 This repository contains the checkpoint for the [IT5 Small](https://huggingface.co/gsarti/it5-small) model fine-tuned on question generation on the [SQuAD-IT corpus](https://huggingface.co/datasets/squad_it) as part of the experiments of the paper [IT5: Large-scale Text-to-te... | 19c4fcc6480356d57049f6f417329ccd |
apache-2.0 | ['italian', 'sequence-to-sequence', 'question-generation', 'squad_it', 'text2text-generation'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines qg = pipeline("text2text-generation", model='it5/it5-small-question-generation') qg("Le conoscenze mediche erano stagnanti durante il Me... | fa9b0fca5aaee2b82a7505570524bcbf |
apache-2.0 | [] | false | Perceiver IO for language Perceiver IO model pre-trained on the Masked Language Modeling (MLM) task proposed in [BERT](https://arxiv.org/abs/1810.04805) using a large text corpus obtained by combining [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). It was i... | 3ada85f3730b8e8807dcecc56334db73 |
apache-2.0 | [] | false | Model description Perceiver IO is a transformer encoder model that can be applied on any modality (text, images, audio, video, ...). The core idea is to employ the self-attention mechanism on a not-too-large set of latent vectors (e.g. 256 or 512), and only use the inputs to perform cross-attention with the latents. ... | 7aa5c3bcc898ec241f2553253f4adfad |
apache-2.0 | [] | false | Intended uses & limitations You can use the raw model for masked language modeling, but the model is intended to be fine-tuned on a labeled dataset. See the [model hub](https://huggingface.co/models?search=deepmind/perceiver) to look for fine-tuned versions on a task that interests you. | 2b017f03585a935676070a0d0ff9fb24 |
apache-2.0 | [] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import PerceiverTokenizer, PerceiverForMaskedLM tokenizer = PerceiverTokenizer.from_pretrained("deepmind/language-perceiver") model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver") text = "This is an incomplete se... | 46362c4854509853672a399e14581234 |
apache-2.0 | [] | false | mask " missing.". Note that the model performs much better if the masked span starts with a space. encoding.input_ids[0, 52:61] = tokenizer.mask_token_id inputs, input_mask = encoding.input_ids.to(device), encoding.attention_mask.to(device) | c35be4cf331f8cf14dac4d9cbebe5a06 |
apache-2.0 | [] | false | forward pass outputs = model(inputs=inputs, attention_mask=input_mask) logits = outputs.logits masked_tokens_predictions = logits[0, 51:61].argmax(dim=-1) print(tokenizer.decode(masked_tokens_predictions)) >>> should print " missing." ``` | 51b8edd23806d768aa1a21fc79d0cd83 |
apache-2.0 | [] | false | Training data This model was pretrained on a combination of [English Wikipedia](https://huggingface.co/datasets/wikipedia) and [C4](https://huggingface.co/datasets/c4). 70% of the training tokens were sampled from the C4 dataset and the remaining 30% from Wikipedia. The authors concatenate 10 documents before splitti... | c318950a456b5c14f1bd880b59326fd2 |
apache-2.0 | ['exbert', 'security', 'cybersecurity', 'cyber security', 'threat hunting', 'threat intelligence'] | false | SecRoBERTa This is the pretrained model presented in [SecBERT: A Pretrained Language Model for Cyber Security Text](https://github.com/jackaduma/SecBERT/), which is a SecRoBERTa model trained on cyber security text. The training corpus was papers taken from * [APTnotes](https://github.com/kbandla/APTnotes) * [Stu... | d618d068391ba0129767813a51bdbe53 |
apache-2.0 | ['exbert', 'security', 'cybersecurity', 'cyber security', 'threat hunting', 'threat intelligence'] | false | **Fill Mask** We proposed to build language model which work on cyber security text, as result, it can improve downstream tasks (NER, Text Classification, Semantic Understand, Q&A) in Cyber Security Domain. First, as below shows Fill-Mask pipeline in [Google Bert](), [AllenAI SciBert](https://github.com/allenai/scib... | 593b7bb2e53ba8432a6331abeed33499 |
apache-2.0 | ['translation'] | false | opus-mt-en-lg * source languages: en * target languages: lg * OPUS readme: [en-lg](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-lg/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 6dd7bcc2d299cebc8c7b2edf8b10f37a |
apache-2.0 | ['generated_from_keras_callback'] | false | Imene/vit-base-patch16-384-wi3 This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2020 - Train Accuracy: 0.9984 - Train Top-3-accuracy: 0.9997 - Validati... | 76d697f6f701dca5c1cefef20a3e4995 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 1200, 'end_learning_ra... | 806a286eb1631eecaec77dbf017e64a0 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 3.6575 | 0.0902 | 0.1945 ... | dffd60b98004d90e89cab0812ba6bb39 |
apache-2.0 | ['generated_from_trainer'] | false | ner_ANAT_DISO This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0746 - Anat Precision: 0.6512 - Anat Recall: 0.6573 - Anat F1: 0.6542 - Anat Number: 534 - Dis... | a08f461707017774d79d37dc6550c3ef |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:---... | 94f56c71d774a5cca8ee95e407e0a06b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0578 - Precision: 0.9189 - Recall: 0.9357 - F1: 0.9272 - Accuracy: 0.9831 | f7cf12bd54512f832e568c6bb9d45953 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0754 | 1.0 | 1756 | 0.0578 | 0.9189 | 0.9357 | 0.9272 | 0.9831 | | e9a91e40de2ab24fcf1d4c3853e21398 |
cc-by-4.0 | ['bert'] | false | bert-fc-medium A medium-size BERT Language Model with a **first character** prediction pre-training objective. For more details about the pre-training objective and the pre-training hyperparameters, please refer to [How does the pre-training objective affect what large language models learn about linguistic properties... | 5b2178638a9b871d45d637476af509cf |
apache-2.0 | ['translation'] | false | opus-mt-uk-sv * source languages: uk * target languages: sv * OPUS readme: [uk-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/uk-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 7b0d1455eddc27ad44b58a1e5d5dbc0b |
other | ['vision', 'image-segmentation'] | false | SegFormer (b4-sized) model fine-tuned on CityScapes SegFormer model fine-tuned on CityScapes at resolution 512x1024. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repositor... | 5ed56bd1bc8ed09c6f0c28c77fc76766 |
other | ['vision', 'image-segmentation'] | 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 SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretr... | dd7f865e13fefeb2bb63e70579c4e6f3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-misogyny-sexism-4tweets-3e-05-0.01 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1537 - Accuracy: 0.6647 - F1: 0.6788 - Precision: 0.6076 - ... | 692f16b02bf43ca79923fde332bf3884 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:---:|:---:|:---:|:---:| | 0.4449 | 1.0 | 1655 | 0.6853 | ... | 981580a1b69c07d026971a566979a63b |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-small-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-luw-upos... | d99a4ea934430624864515ebbc4d0046 |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-small-japanese-aozora") ``` | cd480c71b6973e6188b5ca29a6e53e1a |
mit | ['question generation'] | false | german-qg-t5-e2e-quad (Work in progress) This model is a end-to-end question generation model in German. Given a text, it generates several questions about it. This model is a fine-tuned version of [valhalla/t5-base-e2e-qg](https://huggingface.co/valhalla/t5-base-e2e-qg) on the [GermanQuAD dataset from deepset](https... | 041801ca2c97c5867ced5bddb9bd3574 |
mit | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | 7fecca0e15dfb7fbf0d8d6c48d7a1a45 |
mit | [] | false | Rishusei style on Stable Diffusion This is the `<crishusei-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You... | 88d4917332765a286d3e0e1a16d2a03d |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased.CEBaB_confounding.food_service_positive.sa.5-class.seed_43 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the OpenTable OPENTABLE dataset. It achieves the following results on the evaluation set: - Loss: 0.7961 - Accuracy: 0.6569 - Macro-f1: 0.... | 780b30462e77bed8fa17bc6886c1caa7 |
apache-2.0 | [] | false | PaddlePaddle/uie-m-large Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. The unified text-to-structure generation framework, namely UIE, can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general ... | bd9248804d8351f46240e48320681d2d |
apache-2.0 | ['generated_from_trainer'] | false | model_syllable_onSet3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1590 - 0 Precision: 0.9688 - 0 Recall: 1.0 - 0 F1-score: 0.9841 - 0 Support: 31 ... | d0307700d0faa9fbb0fd6923468f2c74 |
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