Datasets:

librarian-bots

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arham061/distilhubert-finetuned-RHD_Dataset

arham061

transformers

audio-classification

--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - audiofolder metrics: - accuracy model-index: - name: distilhubert-finetuned-RHD_Dataset results: - task: name: Audio Classification type: audio-classification dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8048780487804879 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-RHD_Dataset This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9447 - Accuracy: 0.8049 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0412 | 1.0 | 46 | 1.0084 | 0.6829 | | 0.8547 | 2.0 | 92 | 0.8433 | 0.6585 | | 0.7936 | 3.0 | 138 | 0.7128 | 0.7073 | | 0.5984 | 4.0 | 184 | 0.7778 | 0.7317 | | 0.3888 | 5.0 | 230 | 0.6361 | 0.7317 | | 0.4947 | 6.0 | 276 | 0.7471 | 0.7805 | | 0.1663 | 7.0 | 322 | 0.8244 | 0.7561 | | 0.1379 | 8.0 | 368 | 0.7986 | 0.8049 | | 0.0405 | 9.0 | 414 | 0.8892 | 0.8049 | | 0.0229 | 10.0 | 460 | 0.9447 | 0.8049 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

AnveshakR/Reddit-NFL-FineTuned-Model

AnveshakR

Entry not found

MidPrepAdobe/test_1

MidPrepAdobe

--- license: apache-2.0 ---

vishwa27/flan-t5-large-mawpnli-calcx-nli-pt

vishwa27

transformers

text2text-generation

--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-large-mawpnli-calcx-nli-pt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-large-mawpnli-calcx-nli-pt This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1217 - Rouge1: 95.7098 - Rouge2: 89.9271 - Rougel: 95.5836 - Rougelsum: 95.5842 - Gen Len: 10.9151 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.2279 | 1.0 | 819 | 0.1290 | 95.075 | 87.8764 | 94.7902 | 94.8057 | 10.7978 | | 0.0612 | 2.0 | 1638 | 0.1012 | 95.6219 | 89.6809 | 95.4399 | 95.4521 | 10.9029 | | 0.0418 | 3.0 | 2457 | 0.0972 | 95.7709 | 90.1703 | 95.613 | 95.637 | 10.9328 | | 0.0272 | 4.0 | 3276 | 0.1174 | 95.7478 | 90.1332 | 95.5931 | 95.6069 | 10.9395 | | 0.0215 | 5.0 | 4095 | 0.1217 | 95.7098 | 89.9271 | 95.5836 | 95.5842 | 10.9151 | ### Framework versions - Transformers 4.35.2 - Pytorch 1.12.1+cu113 - Datasets 2.15.0 - Tokenizers 0.15.0

aaalby/asean

aaalby

--- license: openrail ---

dvshah13/q-FrozenLake-v1-4x4-noSlippery

dvshah13

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="dvshah13/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

bodam/sd-model-finetuned-dreambooth-lora

bodam

Entry not found

DJ7/DJ14

DJ7

Entry not found

hkivancoral/smids_5x_deit_tiny_adamax_001_fold1

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: facebook/deit-small-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_tiny_adamax_001_fold1 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8898163606010017 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_5x_deit_tiny_adamax_001_fold1 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9489 - Accuracy: 0.8898 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3549 | 1.0 | 376 | 0.4844 | 0.8264 | | 0.2678 | 2.0 | 752 | 0.3259 | 0.8798 | | 0.3098 | 3.0 | 1128 | 0.3469 | 0.8548 | | 0.2057 | 4.0 | 1504 | 0.3089 | 0.8831 | | 0.15 | 5.0 | 1880 | 0.4280 | 0.8748 | | 0.0947 | 6.0 | 2256 | 0.5773 | 0.8581 | | 0.1544 | 7.0 | 2632 | 0.3805 | 0.8881 | | 0.1085 | 8.0 | 3008 | 0.4878 | 0.8731 | | 0.0399 | 9.0 | 3384 | 0.4495 | 0.8965 | | 0.0251 | 10.0 | 3760 | 0.5573 | 0.8681 | | 0.0684 | 11.0 | 4136 | 0.4467 | 0.8648 | | 0.0506 | 12.0 | 4512 | 0.5126 | 0.8982 | | 0.0075 | 13.0 | 4888 | 0.8575 | 0.8715 | | 0.0481 | 14.0 | 5264 | 0.7463 | 0.8664 | | 0.0077 | 15.0 | 5640 | 0.6816 | 0.8865 | | 0.0098 | 16.0 | 6016 | 0.6312 | 0.8831 | | 0.0003 | 17.0 | 6392 | 0.7022 | 0.8965 | | 0.0075 | 18.0 | 6768 | 0.6976 | 0.8731 | | 0.0042 | 19.0 | 7144 | 0.6012 | 0.8881 | | 0.0311 | 20.0 | 7520 | 0.7693 | 0.8932 | | 0.003 | 21.0 | 7896 | 0.6254 | 0.8915 | | 0.0101 | 22.0 | 8272 | 0.6004 | 0.8998 | | 0.0209 | 23.0 | 8648 | 0.7643 | 0.8815 | | 0.0001 | 24.0 | 9024 | 0.8262 | 0.8848 | | 0.0007 | 25.0 | 9400 | 0.6944 | 0.8898 | | 0.0034 | 26.0 | 9776 | 0.7140 | 0.8915 | | 0.0071 | 27.0 | 10152 | 0.8088 | 0.8798 | | 0.0001 | 28.0 | 10528 | 0.7766 | 0.9032 | | 0.0039 | 29.0 | 10904 | 0.8084 | 0.8948 | | 0.0045 | 30.0 | 11280 | 0.7741 | 0.8831 | | 0.0006 | 31.0 | 11656 | 0.8264 | 0.8932 | | 0.0 | 32.0 | 12032 | 0.8432 | 0.8865 | | 0.0 | 33.0 | 12408 | 0.8641 | 0.8848 | | 0.0 | 34.0 | 12784 | 0.8447 | 0.8865 | | 0.0 | 35.0 | 13160 | 0.8402 | 0.8848 | | 0.0 | 36.0 | 13536 | 0.8232 | 0.8948 | | 0.0 | 37.0 | 13912 | 0.8382 | 0.8915 | | 0.0 | 38.0 | 14288 | 0.8652 | 0.8898 | | 0.0 | 39.0 | 14664 | 0.8733 | 0.8848 | | 0.0 | 40.0 | 15040 | 0.8254 | 0.8881 | | 0.0 | 41.0 | 15416 | 0.8627 | 0.8848 | | 0.0 | 42.0 | 15792 | 0.8799 | 0.8881 | | 0.0 | 43.0 | 16168 | 0.8887 | 0.8915 | | 0.0 | 44.0 | 16544 | 0.9046 | 0.8932 | | 0.0 | 45.0 | 16920 | 0.9092 | 0.8932 | | 0.0031 | 46.0 | 17296 | 0.9143 | 0.8881 | | 0.0 | 47.0 | 17672 | 0.9293 | 0.8915 | | 0.0 | 48.0 | 18048 | 0.9378 | 0.8898 | | 0.0 | 49.0 | 18424 | 0.9447 | 0.8898 | | 0.0023 | 50.0 | 18800 | 0.9489 | 0.8898 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

idontgoddamn/AsagiMutsuki

idontgoddamn

Entry not found

Santiclibrain/mixtral_no_robots

Santiclibrain

Entry not found

Arlech/GameTL

Arlech

Entry not found

nhihlle/whisper-small-vietnamese

nhihlle

transformers

automatic-speech-recognition

--- language: - vi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer metrics: - wer model-index: - name: Whisper Small Vietnamese - Nhi Le results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Vietnamese - Nhi Le This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Vietnamese ASR Custom Corpus dataset. It achieves the following results on the evaluation set: - Loss: 2.3541 - Wer: 56.0841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 4.6449 | 0.02 | 2 | 4.3170 | 30.7461 | | 3.4276 | 0.04 | 4 | 2.9799 | 32.8493 | | 2.7302 | 0.07 | 6 | 2.6128 | 30.0451 | | 2.0397 | 0.09 | 8 | 2.4305 | 33.7506 | | 2.0823 | 0.11 | 10 | 2.3541 | 56.0841 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.1+cpu - Datasets 2.15.0 - Tokenizers 0.15.0

showrounak/moviesong

showrounak

Entry not found

aaptknews/BHARAT-AI

aaptknews

transformers

text-generation

--- license: gpl-3.0 library_name: transformers pipeline_tag: text-generation datasets: - fka/awesome-chatgpt-prompts - wikimedia/wikipedia - Lin-Chen/ShareGPT4V language: - en - hi - bh metrics: - accuracy tags: - code --- pip install transformers

dvshah13/taxi-v3

dvshah13

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="dvshah13/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

LoneStriker/bagel-dpo-7b-v0.1-3.0bpw-h6-exl2-2

LoneStriker

transformers

text-generation

--- license: apache-2.0 --- # A bagel, with everything (including DPO)  ## Overview This is the DPO'd version of https://huggingface.co/jondurbin/bagel-7b-v0.1 If you are getting too many AALLM or other refusals, even with explicitly human system prompts, you may want to try the non-DPO version. ## Benchmarks I ran these against the latest main branch of lm-evaluation-harness (and opencompass/FastChat for agieval and mt-bench), since batch size/etc effects score for some benchmarks. | model | arc_challenge | boolq | gsm8k | hellaswag | mmlu | openbookqa | piqa | truthful_qa | winogrande | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | bagel | __0.6715__ | 0.8813 | __0.5618__ | 0.8397 | __0.6408__ | __0.51__ | __0.8406__ | __0.6275__ | __0.7561__ | | openhermes-2.5 | 0.6476 | __0.8835__ | 0.4852 | __0.8414__ | 0.6347 | 0.498 | 0.8400 | 0.5295 | 0.7443 | MT-Bench: ``` ########## First turn ########## score model turn bagel-7b-v0.1 1 7.60625 ########## Second turn ########## score model turn bagel-7b-v0.1 2 7.00625 ########## Average ########## score model bagel-7b-v0.1 7.30625 ``` ## Data selection. The first step in the process is creating a dataset. In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data. All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with. See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source. Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them). This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken. ### SFT data sources *Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check* - [ai2_arc](https://huggingface.co/datasets/ai2_arc) - Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent. - [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1) - Variety of categories of synthetic instructions generated by gpt-4. - [apps](https://huggingface.co/datasets/codeparrot/apps) - Python coding dataset with 10k problems. - [belebele](https://huggingface.co/datasets/facebook/belebele) - Multi-lingual reading comprehension dataset. - [boolq](https://huggingface.co/datasets/boolq) - Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?) - [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text) - RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be. - [drop](https://huggingface.co/datasets/drop) - More reading comprehension. - [gutenberg](https://www.gutenberg.org/) (plain text) - Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize) - [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO) - Chats collected by the lmsys chat arena, containing a wide variety of chats with various models. - [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) - Composite dataset with a variety of math-related tasks and problem/question formats. - [mmlu](https://huggingface.co/datasets/cais/mmlu) - Massive Multitask Language Understanding - a wide variety of questions about various subject matters. - [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions) - Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type) - [openbookqa](https://huggingface.co/datasets/openbookqa) - Question answering dataset. - [piqa](https://huggingface.co/datasets/piqa) - Phyiscal interaction question answering. - [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca) - Python instruction response pairs, validated as functional. - [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code) - Code problems and solutions in a variety of programming languages taken from rosettacode.org. - [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca) - Collection of ~500k gpt-4 verified chats from OpenOrca. - [spider](https://huggingface.co/datasets/spider) - SQL-targeted dataset. - [squad_v2](https://huggingface.co/datasets/squad_v2) - Contextual question answering (RAG). - [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3) - GPT-4 generated data using advanced prompting from Migel Tissera. - [winogrande](https://huggingface.co/datasets/winogrande) - Fill in the blank style prompts. ### DPO data sources - [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) - The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen" - [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer) - Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected" - [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) - Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset. - [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1) - __*highly toxic and potentially illegal content!*__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering. - [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) - DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc. - [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned) - One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included. Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss). ### Total dataset size The deduplicated and decontamined list of instructions contains 1,671,822 items: - 1,602,217 SFT/instructions - 59,247 DPO pairs - 1606 with both SFT and DPO data Keep in mind, this number becomes 4x larger when applying the various prompt formats. ## Prompt formatting In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta). I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format. This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate. ### Alpaca (sort of) ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {system prompt, if provided} {instruction} ### Response: ``` The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section. ### Vicuna ``` {system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."} USER: {instruction} ASSISTANT: ``` ### ChatML (sort of) I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong). So, instead of: ```text {bos}<|im_start|>{role} {text} <|im_end|>{eos} ``` I just changed it to: ```text {bos}{role} {text} {eos} ``` In practice, this would mean tokenization code like such: ```python tokenizer = AutoTokenizer.from_pretrained('mistralai/mistral-7b-v0.1') input_str = f"""system You are a goat. {tokenizer.eos_token} {tokenizer.bos_token}user Tell me how to fry an egg. {tokenizer.eos_token} {tokenizer.bos_token}assistant """ inputs = tokenizer(input_str, return_tensors="pt") ``` If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune. ### Llama-2 chat ``` [INST] <<SYS>> {system} <</SYS>> {instruction} [/INST] ``` ## Fine tuning ### SFT phase An example for mistral-7b: *Note: I actually used my fork of [qlora](https://github.com/jondurbin/qlora)'s `train.py` for this, but I'm porting it to a minified version here, not tested yet!* *More notes: I stopped the SFT phase around 50% because of budget constraints.* ```bash export BASE_DIR=/workspace export WANDB_API_KEY=[redacted] export WANDB_PROJECT=bagel-7b-v0.1 # Run the pretraining. accelerate launch bagel/tune/sft.py \ --model_name_or_path $BASE_DIR/mistral-7b \ --final_output_dir $BASE_DIR/$WANDB_PROJECT \ --output_dir $BASE_DIR/$WANDB_PROJECT-workdir \ --num_train_epochs 1 \ --logging_steps 1 \ --save_strategy steps \ --save_steps 200 \ --save_total_limit 5 \ --data_seed 42 \ --evaluation_strategy steps \ --eval_dataset_size 0.0006 \ --eval_steps 200 \ --max_new_tokens 4096 \ --dataloader_num_workers 3 \ --logging_strategy steps \ --remove_unused_columns False \ --do_train \ --full_finetune \ --bf16 \ --bits 16 \ --optim adamw_torch \ --lr_scheduler_type linear \ --dataset $BASE_DIR/bagel/bagel-input-output-v0.1.parquet \ --dataset_format input-output \ --model_max_len 4096 \ --per_device_train_batch_size 8 \ --learning_rate 3.5e-7 \ --warmup_ratio 0.005 \ --adam_beta2 0.999 \ --max_grad_norm 0.3 \ --weight_decay 0.001 \ --seed 42 \ --report_to wandb \ --gradient_checkpointing True \ --gradient_accumulation_steps 4 \ --skip_excess_length False \ --ddp_find_unused_parameters False \ --use_flash_attention_2 \ --deepspeed deepspeed.json ``` Deepspeed configuration: ```json { "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "bf16": { "enabled": true }, "zero_optimization": { "stage": 2, "contiguous_gradients": true, "overlap_comm": true, "reduce_scatter": true, "reduce_bucket_size": 5e8, "allgather_bucket_size": 5e8 } } ``` ### DPO phase An example of the DPO phase for mistral-7b (requires first running the SFT): ```bash export BASE_DIR=/mnt/data export WANDB_API_KEY=[redacted] export WANDB_PROJECT=bagel-dpo-7b-v0.1 accelerate launch bagel/tune/dpo.py \ --model_name_or_path bagel-7b-v0.1 \ --learning_rate 3e-7 \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --max_length 4096 \ --max_prompt_length 1024 \ --max_target_length 3092 \ --num_train_epochs 3 \ --report_to wandb \ --gradient_checkpointing true \ --use_flash_attention_2 true \ --dataset $BASE_DIR/bagel/bagel-dpo-v0.1.parquet \ --eval_steps 5 \ --eval_dataset_size 0.03 \ --workdir $BASE_DIR/$WANDB_PROJECT-workdir \ --output_dir $BASE_DIR/$WANDB_PROJECT \ --deepspeed deepspeed.json \ --save_steps 25 \ --save_total_limit 5 ```

JAILHJH/TRTRT

JAILHJH

--- license: openrail ---

Tsuinzues/tori

Tsuinzues

--- license: openrail ---

brendenbogi/idk

brendenbogi

Entry not found

JeskoR/mistral_b_finance_finetuned_test

JeskoR

peft

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.2.dev0

erbacher/vae-burgers-norevin

erbacher

transformers

Entry not found

Santiclibrain/mixtral_orca_spanish_adapter

Santiclibrain

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mixtral-8x7B-v0.1 model-index: - name: mixtral_no_robots_secondtry results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mixtral_no_robots_secondtry This model is a fine-tuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9807 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 8 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0635 | 0.02 | 1000 | 1.1332 | | 0.9311 | 0.03 | 2000 | 1.1109 | | 0.9417 | 0.05 | 3000 | 1.0926 | | 1.0411 | 0.06 | 4000 | 1.0809 | | 0.9516 | 0.08 | 5000 | 1.0786 | | 1.0107 | 0.09 | 6000 | 1.0726 | | 1.0698 | 0.11 | 7000 | 1.0666 | | 1.1083 | 0.13 | 8000 | 1.0638 | | 0.9148 | 0.14 | 9000 | 1.0589 | | 0.957 | 0.16 | 10000 | 1.0565 | | 1.0063 | 0.17 | 11000 | 1.0531 | | 0.9831 | 0.19 | 12000 | 1.0509 | | 1.0826 | 0.2 | 13000 | 1.0490 | | 0.9598 | 0.22 | 14000 | 1.0518 | | 0.8066 | 0.23 | 15000 | 1.0453 | | 0.8795 | 0.25 | 16000 | 1.0431 | | 1.1402 | 0.27 | 17000 | 1.0442 | | 1.0652 | 0.28 | 18000 | 1.0428 | | 0.93 | 0.3 | 19000 | 1.0371 | | 0.9727 | 0.31 | 20000 | 1.0344 | | 1.0753 | 0.33 | 21000 | 1.0339 | | 0.9498 | 0.34 | 22000 | 1.0303 | | 0.6971 | 0.36 | 23000 | 1.0316 | | 0.9259 | 0.38 | 24000 | 1.0298 | | 1.0359 | 0.39 | 25000 | 1.0284 | | 1.1883 | 0.41 | 26000 | 1.0273 | | 0.8642 | 0.42 | 27000 | 1.0250 | | 0.9147 | 0.44 | 28000 | 1.0226 | | 0.7824 | 0.45 | 29000 | 1.0237 | | 0.8319 | 0.47 | 30000 | 1.0219 | | 0.9443 | 0.49 | 31000 | 1.0190 | | 0.9103 | 0.5 | 32000 | 1.0166 | | 0.8903 | 0.52 | 33000 | 1.0149 | | 1.0509 | 0.53 | 34000 | 1.0148 | | 1.0008 | 0.55 | 35000 | 1.0151 | | 0.778 | 0.56 | 36000 | 1.0106 | | 0.7957 | 0.58 | 37000 | 1.0090 | | 0.8679 | 0.6 | 38000 | 1.0085 | | 1.064 | 0.61 | 39000 | 1.0064 | | 0.823 | 0.63 | 40000 | 1.0061 | | 0.9117 | 0.64 | 41000 | 1.0047 | | 0.8284 | 0.66 | 42000 | 1.0019 | | 0.9345 | 0.67 | 43000 | 1.0012 | | 0.9854 | 0.69 | 44000 | 1.0004 | | 0.7631 | 0.7 | 45000 | 0.9989 | | 0.7189 | 0.72 | 46000 | 0.9979 | | 0.9386 | 0.74 | 47000 | 0.9952 | | 1.011 | 0.75 | 48000 | 0.9943 | | 0.9627 | 0.77 | 49000 | 0.9941 | | 1.1317 | 0.78 | 50000 | 0.9923 | | 1.0506 | 0.8 | 51000 | 0.9912 | | 0.8596 | 0.81 | 52000 | 0.9894 | | 0.9702 | 0.83 | 53000 | 0.9889 | | 1.0198 | 0.85 | 54000 | 0.9875 | | 1.1125 | 0.86 | 55000 | 0.9862 | | 0.9356 | 0.88 | 56000 | 0.9862 | | 0.7212 | 0.89 | 57000 | 0.9852 | | 0.974 | 0.91 | 58000 | 0.9843 | | 0.9369 | 0.92 | 59000 | 0.9829 | | 0.938 | 0.94 | 60000 | 0.9826 | | 0.8011 | 0.96 | 61000 | 0.9818 | | 0.7937 | 0.97 | 62000 | 0.9811 | | 0.9679 | 0.99 | 63000 | 0.9807 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

danielssj88/platzi-vit-model-omar-espejel

danielssj88

Entry not found

chaosmonk/ag2

chaosmonk

Entry not found

hkivancoral/smids_3x_beit_base_sgd_0001_fold2

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_sgd_0001_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7886855241264559 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_sgd_0001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5470 - Accuracy: 0.7887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1643 | 1.0 | 225 | 1.2557 | 0.3494 | | 1.1336 | 2.0 | 450 | 1.1964 | 0.3727 | | 1.0702 | 3.0 | 675 | 1.1415 | 0.3960 | | 1.0744 | 4.0 | 900 | 1.0897 | 0.4226 | | 0.9272 | 5.0 | 1125 | 1.0392 | 0.4526 | | 0.9348 | 6.0 | 1350 | 0.9924 | 0.4908 | | 0.9221 | 7.0 | 1575 | 0.9474 | 0.5374 | | 0.8806 | 8.0 | 1800 | 0.9069 | 0.5890 | | 0.8541 | 9.0 | 2025 | 0.8693 | 0.6206 | | 0.8102 | 10.0 | 2250 | 0.8367 | 0.6439 | | 0.7893 | 11.0 | 2475 | 0.8072 | 0.6672 | | 0.7786 | 12.0 | 2700 | 0.7812 | 0.6872 | | 0.7601 | 13.0 | 2925 | 0.7581 | 0.7038 | | 0.7654 | 14.0 | 3150 | 0.7376 | 0.7105 | | 0.7556 | 15.0 | 3375 | 0.7195 | 0.7171 | | 0.7319 | 16.0 | 3600 | 0.7031 | 0.7321 | | 0.6868 | 17.0 | 3825 | 0.6881 | 0.7354 | | 0.7278 | 18.0 | 4050 | 0.6745 | 0.7421 | | 0.6222 | 19.0 | 4275 | 0.6623 | 0.7454 | | 0.6905 | 20.0 | 4500 | 0.6515 | 0.7471 | | 0.6715 | 21.0 | 4725 | 0.6419 | 0.7554 | | 0.7342 | 22.0 | 4950 | 0.6326 | 0.7554 | | 0.6844 | 23.0 | 5175 | 0.6245 | 0.7621 | | 0.6577 | 24.0 | 5400 | 0.6173 | 0.7654 | | 0.6177 | 25.0 | 5625 | 0.6101 | 0.7687 | | 0.647 | 26.0 | 5850 | 0.6037 | 0.7671 | | 0.6355 | 27.0 | 6075 | 0.5976 | 0.7704 | | 0.6059 | 28.0 | 6300 | 0.5926 | 0.7704 | | 0.5954 | 29.0 | 6525 | 0.5873 | 0.7770 | | 0.6256 | 30.0 | 6750 | 0.5829 | 0.7787 | | 0.6261 | 31.0 | 6975 | 0.5789 | 0.7820 | | 0.5804 | 32.0 | 7200 | 0.5748 | 0.7820 | | 0.5936 | 33.0 | 7425 | 0.5711 | 0.7854 | | 0.5647 | 34.0 | 7650 | 0.5682 | 0.7854 | | 0.6238 | 35.0 | 7875 | 0.5657 | 0.7854 | | 0.5976 | 36.0 | 8100 | 0.5630 | 0.7854 | | 0.5852 | 37.0 | 8325 | 0.5605 | 0.7870 | | 0.5826 | 38.0 | 8550 | 0.5584 | 0.7854 | | 0.5619 | 39.0 | 8775 | 0.5564 | 0.7854 | | 0.5946 | 40.0 | 9000 | 0.5547 | 0.7870 | | 0.5381 | 41.0 | 9225 | 0.5529 | 0.7870 | | 0.5966 | 42.0 | 9450 | 0.5514 | 0.7870 | | 0.588 | 43.0 | 9675 | 0.5504 | 0.7870 | | 0.5705 | 44.0 | 9900 | 0.5494 | 0.7854 | | 0.6073 | 45.0 | 10125 | 0.5486 | 0.7870 | | 0.5915 | 46.0 | 10350 | 0.5480 | 0.7887 | | 0.5988 | 47.0 | 10575 | 0.5476 | 0.7887 | | 0.542 | 48.0 | 10800 | 0.5472 | 0.7887 | | 0.5885 | 49.0 | 11025 | 0.5471 | 0.7887 | | 0.5585 | 50.0 | 11250 | 0.5470 | 0.7887 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

shirzady1934/bert-riddle-finetuned_2choice

shirzady1934

transformers

multiple-choice

--- language: - en license: apache-2.0 base_model: bert-base-uncased tags: - mhs - generated_from_trainer metrics: - accuracy model-index: - name: bert_base_uncased results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert_base_uncased This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the WP dataset. It achieves the following results on the evaluation set: - Loss: 0.5591 - Accuracy: 0.8500 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 23 | 0.5552 | 0.8250 | | No log | 2.0 | 46 | 0.4623 | 0.8250 | | No log | 3.0 | 69 | 0.5304 | 0.8250 | | No log | 4.0 | 92 | 0.5741 | 0.8500 | | No log | 5.0 | 115 | 0.5591 | 0.8500 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

EmmaGthn/results_lora_40_5000_bias

EmmaGthn

Entry not found

hkivancoral/smids_3x_beit_base_rms_001_fold2

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_rms_001_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7737104825291181 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_rms_001_fold2 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8871 - Accuracy: 0.7737 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1063 | 1.0 | 225 | 1.1925 | 0.3627 | | 0.8848 | 2.0 | 450 | 0.8623 | 0.5557 | | 0.9929 | 3.0 | 675 | 0.7924 | 0.5774 | | 0.7922 | 4.0 | 900 | 0.7743 | 0.5973 | | 0.7804 | 5.0 | 1125 | 0.7554 | 0.5940 | | 0.7536 | 6.0 | 1350 | 0.7911 | 0.5740 | | 0.7389 | 7.0 | 1575 | 0.8973 | 0.5524 | | 0.8004 | 8.0 | 1800 | 0.7349 | 0.6140 | | 0.7283 | 9.0 | 2025 | 0.7228 | 0.6356 | | 0.7381 | 10.0 | 2250 | 0.7154 | 0.6389 | | 0.8566 | 11.0 | 2475 | 0.7154 | 0.6373 | | 0.725 | 12.0 | 2700 | 0.6853 | 0.6539 | | 0.7139 | 13.0 | 2925 | 0.6833 | 0.6722 | | 0.708 | 14.0 | 3150 | 0.7156 | 0.6489 | | 0.6892 | 15.0 | 3375 | 0.6841 | 0.6955 | | 0.7392 | 16.0 | 3600 | 0.6648 | 0.6905 | | 0.7123 | 17.0 | 3825 | 0.6864 | 0.6689 | | 0.6752 | 18.0 | 4050 | 0.6534 | 0.7088 | | 0.7193 | 19.0 | 4275 | 0.7054 | 0.6755 | | 0.6734 | 20.0 | 4500 | 0.6500 | 0.6855 | | 0.649 | 21.0 | 4725 | 0.6222 | 0.6872 | | 0.7173 | 22.0 | 4950 | 0.6280 | 0.7321 | | 0.6723 | 23.0 | 5175 | 0.6016 | 0.7587 | | 0.6406 | 24.0 | 5400 | 0.6206 | 0.7221 | | 0.6216 | 25.0 | 5625 | 0.6173 | 0.7338 | | 0.6154 | 26.0 | 5850 | 0.5917 | 0.7488 | | 0.6137 | 27.0 | 6075 | 0.6327 | 0.7304 | | 0.597 | 28.0 | 6300 | 0.6319 | 0.7155 | | 0.6292 | 29.0 | 6525 | 0.6003 | 0.7321 | | 0.615 | 30.0 | 6750 | 0.5967 | 0.7554 | | 0.5842 | 31.0 | 6975 | 0.5866 | 0.7587 | | 0.5976 | 32.0 | 7200 | 0.5968 | 0.7388 | | 0.5096 | 33.0 | 7425 | 0.5717 | 0.7671 | | 0.4883 | 34.0 | 7650 | 0.5888 | 0.7804 | | 0.5258 | 35.0 | 7875 | 0.6027 | 0.7820 | | 0.49 | 36.0 | 8100 | 0.6052 | 0.7820 | | 0.5271 | 37.0 | 8325 | 0.5944 | 0.7654 | | 0.4464 | 38.0 | 8550 | 0.6867 | 0.7504 | | 0.3796 | 39.0 | 8775 | 0.6032 | 0.7820 | | 0.4175 | 40.0 | 9000 | 0.6446 | 0.7704 | | 0.3633 | 41.0 | 9225 | 0.6564 | 0.7804 | | 0.4496 | 42.0 | 9450 | 0.6467 | 0.7770 | | 0.2811 | 43.0 | 9675 | 0.6703 | 0.7754 | | 0.3066 | 44.0 | 9900 | 0.7311 | 0.7754 | | 0.3558 | 45.0 | 10125 | 0.7685 | 0.7787 | | 0.2645 | 46.0 | 10350 | 0.7874 | 0.7754 | | 0.2214 | 47.0 | 10575 | 0.8226 | 0.7737 | | 0.2321 | 48.0 | 10800 | 0.8600 | 0.7704 | | 0.314 | 49.0 | 11025 | 0.8728 | 0.7770 | | 0.1915 | 50.0 | 11250 | 0.8871 | 0.7737 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

Trelis/SUS-Chat-34B-function-calling-v3

Trelis

transformers

text-generation

--- license: other widget: - example_title: SUS-Chat text: hi output: text: ' Hello! How can I assist you today?' pipeline_tag: text-generation tags: - yi - long context - commercial use - gptq - function-calling - function calling extra_gated_prompt: "Purchase access to this repo [HERE](https://buy.stripe.com/6oE9Bmg8t1Dt1ck9BL)!" --- # Function Calling Fine-tuned Yi Chat 200k Context Purchase access to this model [here](https://buy.stripe.com/6oE9Bmg8t1Dt1ck9BL). This model is fine-tuned for function calling. - The function metadata format is the same as used for OpenAI. - The model is suitable for commercial use. - See the 'gptq' branch for the GPTQ model. - AWQ and GGUF are available on request after purchase. Check out other fine-tuned function calling models [here](https://trelis.com/function-calling/). ## Quick Server Setup Runpod one click template, TGI API with EETQ (8bit) [here](https://runpod.io/gsc?template=p5zxy64o61&ref=jmfkcdio). You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model. Runpod one click template, vLLM API with AWQ (4bit) [here](https://runpod.io/gsc?template=no46bznoof&ref=jmfkcdio). You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model. Runpod Affiliate [Link](https://runpod.io?ref=jmfkcdio) (helps support the Trelis channel). ## Inference Scripts See below for sample prompt format. Complete inference scripts are available for purchase [here](https://trelis.com/enterprise-server-api-and-inference-guide/): - Easily format prompts using tokenizer.apply_chat_format (starting from openai formatted functions and a list of messages) - Automate catching, handling and chaining of function calls. ## Prompt Format ``` B_FUNC, E_FUNC = "You have access to the following functions. Use them if required:\n\n", "\n\n" B_INST, E_INST = "### Human: ", "\n\n### Assistant: " #SUSChat prompt = f"{B_INST}{B_FUNC}{functionList.strip()}{E_FUNC}{user_prompt.strip()}{E_INST}\n\n" ``` ### Using tokenizer.apply_chat_template For an easier application of the prompt, you can set up as follows: Set up `messages`: ``` [ { "role": "function_metadata", "content": "FUNCTION_METADATA" }, { "role": "user", "content": "What is the current weather in London?" }, { "role": "function_call", "content": "{\n \"name\": \"get_current_weather\",\n \"arguments\": {\n \"city\": \"London\"\n }\n}" }, { "role": "function_response", "content": "{\n \"temperature\": \"15 C\",\n \"condition\": \"Cloudy\"\n}" }, { "role": "assistant", "content": "The current weather in London is Cloudy with a temperature of 15 Celsius" } ] ``` with `FUNCTION_METADATA` as: ``` [ { "type": "function", "function": { "name": "get_current_weather", "description": "This function gets the current weather in a given city", "parameters": { "type": "object", "properties": { "city": { "type": "string", "description": "The city, e.g., San Francisco" }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use." } }, "required": ["city"] } } }, { "type": "function", "function": { "name": "get_clothes", "description": "This function provides a suggestion of clothes to wear based on the current weather", "parameters": { "type": "object", "properties": { "temperature": { "type": "string", "description": "The temperature, e.g., 15 C or 59 F" }, "condition": { "type": "string", "description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'" } }, "required": ["temperature", "condition"] } } } ] ``` and then apply the chat template to get a formatted prompt: ``` tokenizer = AutoTokenizer.from_pretrained('Trelis/SUS-Chat-34B-function-calling-v3', trust_remote_code=True) prompt = tokenizer.apply_chat_template(prompt, tokenize=False) ``` If you are using a gated model, you need to first run: ``` pip install huggingface_hub huggingface-cli login ``` ### Manual Prompt: ``` Human: You have access to the following functions. Use them if required: [ { "type": "function", "function": { "name": "get_stock_price", "description": "Get the stock price of an array of stocks", "parameters": { "type": "object", "properties": { "names": { "type": "array", "items": { "type": "string" }, "description": "An array of stocks" } }, "required": [ "names" ] } } }, { "type": "function", "function": { "name": "get_big_stocks", "description": "Get the names of the largest N stocks by market cap", "parameters": { "type": "object", "properties": { "number": { "type": "integer", "description": "The number of largest stocks to get the names of, e.g. 25" }, "region": { "type": "string", "description": "The region to consider, can be \"US\" or \"World\"." } }, "required": [ "number" ] } } } ] Get the names of the five largest stocks by market cap Assistant: { "name": "get_big_stocks", "arguments": { "number": 5 } }<|endoftext|> ``` # Dataset See [Trelis/function_calling_v3](https://huggingface.co/datasets/Trelis/function_calling_v3). # License This model may be used commercially for inference according to the terms of the Yi license, or for further fine-tuning and inference. Users may not re-publish or re-sell this model in the same or derivative form (including fine-tunes). ** The SFT chat fine-tuned model's repo card follows below. ** # 🐷SUS-Chat: Instruction tuning done right <p align="left"> <a href="README_CN.md">中文</a>&nbsp ｜ &nbspEnglish&nbsp </p> <br><br> <div align="center"> <p align="center"> <img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/sustech.svg?sanitize=true" width="200px"> <img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/ccnl.png?sanitize=true" width="200px"> </p> <div style="display: inline-block;"> <a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/issues"> <img src="https://img.shields.io/github/issues/SUSTech-IDEA/SUS-Chat?logo=github" style="margin: 0 0;"> </a> </div> <div style="display: inline-block;"> <a href="https://huggingface.co/SUSTech"> <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SUSTech-blue" style="margin: 0 0;"> </a> </div> <div style="display: inline-block;"> <a rel="noopener nofollow" href="https://www.modelscope.cn/organization/sustc/"> <img src="https://img.shields.io/badge/🤖ModelScope-sustc-blue" style="margin: 0 0;"> </a> </div> <a href="https://wisemodel.cn/organization/SUSTech"> <img src="https://img.shields.io/badge/WiseModel-SUSTech-blue"> </a> <div style="display: inline-block;"> <a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/LICENSE"> <img src="https://img.shields.io/badge/Code_License-Apache_2.0-lightblue" style="margin: 0 0;"> </a> </div> <div style="display: inline-block;"> <a rel="noopener nofollow" href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt"> <img src="https://img.shields.io/badge/Model_License-Model_Agreement-lightblue" style="margin: 0 0;"> </a> </div> <div style="display: inline-block;"> <a rel="noopener nofollow" href="mailto:oss@data.sustech.edu.cn"> <img src="https://img.shields.io/badge/✉️-data@sustech.edu.cn-FFE01B" style="margin: 0 0;"> </a> </div> </div> # News - 2023-12-09: 🔥 `Tigerbot` variant has been [deleted](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/438), `SUS-Chat-34B` is now the the top-ranked LLaMA model and the top-ranked chat model. - 2023-12-07: SUS-Chat-34B is now available on [WiseModel🧠](https://wisemodel.cn/model/SUSTech/SUS-Chat-34B). - 2023-12-06: Try [SUS-Chat-34B chat-ui](https://huggingface.co/spaces/SUSTech/SUS-Chat-34B). - 2023-12-05: SUS-Chat-34B is now available on [ModelScope🤖](https://www.modelscope.cn/models/SUSTC/SUS-Chat-34B/summary) - 2023-12-05: SUS-Chat-34B is ranked 2nd in [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) and surpassed all models under 70B. - 2023-12-01: SUS-Chat-34B is now available on [HuggingFace🤗](https://huggingface.co/SUSTech/SUS-Chat-34B). # Introduction <img src="https://hackmd.io/_uploads/HJlDtzhBa.png" id="fig-sus" alt="Figure 1: DALL·E 2023-12-01 11.03.28 - An imposing, majestic wild boar combined with elements of a futuristic transformer robot. The boar itself should be intricately blended with these tra" /> **SUS-Chat-34B** is a 34B bilingual Chinese-English dialogue model, jointly released by the **[Southern University of Science and Technology](https://huggingface.co/SUSTech)** and **[IDEA-CCNL](https://huggingface.co/IDEA-CCNL)**. This model is based on [`01-ai/Yi-34B`](https://huggingface.co/01-ai/Yi-34B) and has been fine-tuned on millions of high-quality, multilingual instruction data. While maintaining the strong language capabilities of the base model, the SUS-Chat-34B model has improved the model’s response to human instructions through high-quality instruction fine-tuning and excels at imitating human thought processes through chains of thought. It introduces inter-instruction attention sharing in long texts, expanding the window size from 4K to 8K, significantly enhancing the usability of multi-turn dialogues. It has surpassed all models of the same size in almost all benchmark tests and is better suited to meet the practical needs of complex multilingual tasks. Compared to larger models, SUS-Chat-34B remains highly competitive and has achieved state-of-the-art performance in our comprehensive evaluations. SUS-Chat-34B model has the following highlights: 1. Large-scale complex instruction following data: Trained with 1.4 billion tokens of high-quality complex instruction data, covering Chinese and English, multi-turn dialogues, mathematics, reasoning, and various other types of instruction data; 2. Strong performance in general tasks: The SUS-Chat-34B model excels in numerous mainstream Chinese and English tasks, surpassing other open-source instruction fine-tuned models of the same parameter scale. It also competes well against models with larger parameter scales; 3. Longer context window and excellent multi-turn dialogue capabilities: Currently, SUS-Chat-34B supports an 8K context window, and is trained with a large amount of multi-turn instruction and single-multi-turn mixed data, demonstrating remarkable capabilities in long-text dialogue information focus and instruction follow-up. SUS-Chat powerfully demonstrates that through the right instruction fine-tuning, academic institutions can achieve better performance without increasing model parameters, using open-source datasets and models. This bridges the gap between academia and industry in large language models and opens new possibilities for collaboration between academic and industrial sectors. # Performance To better evaluate the performance of the SUS-Chat-34B model, we conducted assessments across multiple benchmark tests and have open-sourced the evaluation framework [TLEM](https://huggingface.co/spaces/SUSTech/tlem) to facilitate replication and comparison by other researchers. In TLEM, we utilized various benchmark tests including MMLU, CMMLU, C-Eval, BBH, GSM-8K, and MATH, to measure the model’s knowledge and thinking capabilities. In these metrics, the SUS-Chat-34B model achieved state-of-the-art performance. Additionally, we incorporated [lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) to test SUS-Chat and similar models on winogrande, hellaswag, arc, and truthful-qa, assessing the model’s common-sense reasoning ability and susceptibility to illusions. Overall, the SUS-Chat-34B model significantly outperformed models of similar scale and achieved the most advanced comprehensive performance. <img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/radar.png" id="fig-bench" alt="Figure 2: Benchmark" /> <div> <table> <colgroup> <col style="width: 50%" /> <col style="width: 50%" /> </colgroup> <tbody> <tr class="odd"> <td style="text-align: center;"><div width="50.0%" data-layout-align="center"> <h2 id="english-understanding">English Understanding</h2> <table> <thead> <tr class="header"> <th style="text-align: right;">Model</th> <th style="text-align: center;">mmlu (0-shot)</th> </tr> </thead> <tbody> <tr class="odd"> <td style="text-align: right;">GPT-4</td> <td style="text-align: center;">83</td> </tr> <tr class="even"> <td style="text-align: right;">SUS-Chat-34B</td> <td style="text-align: center;"><u>74.35</u></td> </tr> <tr class="odd"> <td style="text-align: right;">Qwen-72b-Chat</td> <td style="text-align: center;"><strong>74.52</strong></td> </tr> <tr class="even"> <td style="text-align: right;">Deepseek-68b-Chat</td> <td style="text-align: center;">69.43</td> </tr> <tr class="odd"> <td style="text-align: right;">OrionStar-Yi-34B-Chat</td> <td style="text-align: center;">68.51</td> </tr> <tr class="even"> <td style="text-align: right;">Yi-34B-Chat</td> <td style="text-align: center;">66.96</td> </tr> </tbody> </table> </div></td> <td style="text-align: center;"><div width="50.0%" data-layout-align="center"> <h2 id="chinese-capabilities">Chinese Capabilities</h2> <table> <colgroup> <col style="width: 34%" /> <col style="width: 32%" /> <col style="width: 32%" /> </colgroup> <thead> <tr class="header"> <th style="text-align: right;">Model</th> <th style="text-align: center;">cmmlu (0-shot)</th> <th style="text-align: center;">C-Eval (0-shot)<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref">¹</a></th> </tr> </thead> <tbody> <tr class="odd"> <td style="text-align: right;">GPT-4</td> <td style="text-align: center;">71</td> <td style="text-align: center;">69.9</td> </tr> <tr class="even"> <td style="text-align: right;">SUS-Chat-34B</td> <td style="text-align: center;"><strong>78.68</strong></td> <td style="text-align: center;"><strong>82.42</strong></td> </tr> <tr class="odd"> <td style="text-align: right;">Qwen-72b-Chat</td> <td style="text-align: center;"><u>77.02</u></td> <td style="text-align: center;"><u>77.22</u></td> </tr> <tr class="even"> <td style="text-align: right;">Deepseek-68b-Chat</td> <td style="text-align: center;">48.51</td> <td style="text-align: center;">59.7</td> </tr> <tr class="odd"> <td style="text-align: right;">OrionStar-Yi-34B-Chat</td> <td style="text-align: center;">66.88</td> <td style="text-align: center;">65.13</td> </tr> <tr class="even"> <td style="text-align: right;">Yi-34B-Chat</td> <td style="text-align: center;">55.16</td> <td style="text-align: center;">77.16</td> </tr> </tbody> </table> </div></td> </tr> </tbody> </table> <section id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"> <hr /> <ol> <li id="fn1"><p>C-Eval results are evaluated on the validation datasets<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li> </ol> </section> </div> ## Math & Reasoning | Model | gsm8k (0-shot) | MATH (0-shot) | BBH (0-shot) | |----------------------:|:--------------:|:-------------:|:------------:| | GPT-4 | 91.4 | 45.8 | 86.7 | | SUS-Chat-34B | **80.06** | 28.7 | 67.62 | | Qwen-72b-Chat | <u>76.57</u> | **35.9** | **72.63** | | Deepseek-68b-Chat | 74.45 | <u>29.56</u> | <u>69.73</u> | | OrionStar-Yi-34B-Chat | 54.36 | 12.8 | 62.88 | | Yi-34B-Chat | 63.76 | 10.02 | 61.54 | ## More Tasks | Model | winogrande (5-shot) | arc (25-shot) | hellaswag (10-shot) | TruthfulQA mc1 (0-shot) | TruthfulQA mc2 (0-shot) | |----------------------:|:-------------------:|:-------------:|:-------------------:|:-----------------------:|:-----------------------:| | GPT-4 | — | 94.5 | 91.4 | 59.00 | — | | SUS-Chat-34B | **81.22** | <u>81.54</u> | 83.79 | **40.64** | **57.47** | | Qwen-72b-Chat | 76.09 | **82.10** | <u>86.06</u> | 39.17 | <u>56.37</u> | | Deepseek-68b-Chat | <u>80.58</u> | 81.29 | **87.02** | <u>40.02</u> | 50.64 | | OrionStar-Yi-34B-Chat | 77.27 | 80.19 | 84.54 | 36.47 | 53.24 | | Yi-34B-Chat | 76.64 | 70.66 | 82.29 | 38.19 | 54.57 | ## Overall | Model | Average | |----------------------:|:---------:| | SUS-Chat-34B | **69.05** | | Qwen-72b-Chat | 68.41 | | Deepseek-68b-Chat | 62.91 | | OrionStar-Yi-34B-Chat | 60.21 | | Yi-34B-Chat | 59.72 | To reproduce the results, please start a corresponding vllm server and refer to [here](https://sustech-tlem.static.hf.space/index.html#start-evaluating-your-model-in-3-line). # Usage SUS-Chat-34B is a standard LLaMA model and should be seamlessly compatible with the LLaMA ecosystem. We provide the following example to demonstrate how it can be used for multi-turn dialogues. Feel free to [open an issue](https://github.com/SUSTech-IDEA/SUS-Chat/issues) if you have any questions. ``` python from transformers import AutoModelForCausalLM, AutoTokenizer # 🤗 Transformers, or # from modelscope import AutoModelForCausalLM, AutoTokenizer # 🤖 ModelScope def chat_template(messages): history = "" for message in messages: match message: case {"role": "user", "content": message}: history += f"### Human: {message}\n\n### Assistant: " case {"role": "assistant", "content": message}: history += message return history model_path = "SUSTech/SUS-Chat-34B" # model_path = "SUSTC/SUS-Chat-34B" # ModelScope tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype="auto" ).eval() messages = [{"role": "user", "content": "hi"}] input_ids = tokenizer.encode( chat_template(messages), return_tensors="pt", add_special_tokens=False ).to("cuda") output_ids = model.generate(input_ids.to("cuda"), max_length=256) response = tokenizer.decode( output_ids[0][input_ids.shape[1] :], skip_special_tokens=False ) messages.append({"role": "assistant", "content": response}) # Second round messages.append({"role": "user", "content": "What is the capital of China?"}) input_ids = tokenizer.encode( chat_template(messages), return_tensors="pt", add_special_tokens=False ).to("cuda") output_ids = model.generate(input_ids.to("cuda"), max_length=256) response = tokenizer.decode( output_ids[0][input_ids.shape[1] :], skip_special_tokens=False ) messages.append({"role": "assistant", "content": response}) ``` # Limitations SUS-Chat has only undergone supervised fine-tuning and has not yet been trained on human preference learning. As a result, it may produce unreasonable responses in some situations and exacerbate existing issues in language models, including hallucinations, non-determinism, and cumulative errors. To achieve better performance for downstream tasks, we recommend adjusting the generation configuration parameters accordingly. # Disclaimer During the training process, we used data compliance check algorithms to ensure the compliance of the training model as much as possible. Due to the complexity of the data and the diverse use cases of language models, we cannot guarantee that the model will produce correct and reasonable outputs in all scenarios. Please be aware that there is still a risk of the model generating problematic outputs. We will not be responsible for any risks or issues arising from misuse, misguidance, illegal use, and related misinformation, as well as data security issues related to the model. # License This model is developed entirely for academic research and free commercial use, but it must adhere to the [license](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt) from [01-ai](https://huggingface.co/01-ai).

OpenNMT/mixtral-onmt-awq-gemv

OpenNMT

This is the OpenNMT-py converted version of Mixtral 8x7b, 4-bit AWQ quantized. The safetensors file is 24GB hence needs 2x24GB GPUs (3090 or 4090) or 1x48GB (A6000). To run the model on 2 GPU the config file needs to have: world_size: 2 gpu_ranks: [0, 1] parallel_mode: "tensor_parallel" If you are lucky to have a A6000 (or V/A/H100 with more than 32GB), then use: world_size: 1 gpu_ranks: [0] #parallel_mode: "tensor_parallel" Command line to run is: `python onmt/bin/translate.py --config /pathto/mixtral-inference-awq.yaml --src /pathto/input-vicuna.txt --output /pathto/mistral-output.txt` Where for instance, input-vicuna.txt contains: `USER:｟newline｠Show me some attractions in Boston.｟newline｠｟newline｠ASSISTANT:｟newline｠` Output will be: `Here are some attractions in Boston:｟newline｠｟newline｠1. Boston Common: This is a historic park located in the heart of Boston. It features a variety of attractions, including the Boston Common Fountain, the Boston Common Bandstand, and the Boston Common Carousel.｟newline｠｟newline｠2. Boston Public Garden: This is a historic park located in the heart of Boston. It features a variety of attractions, including the Boston Public Garden Fountain, the Boston Public Garden Bandstand, and the Boston Public Garden Carousel.｟newline｠｟newline｠3. Boston Museum of Fine Arts: This is a world-renowned art museum located in the heart of Boston. It features a variety of attractions, including the Boston Museum of Fine Arts Fountain, the Boston Museum of Fine Arts Bandstand, and the Boston Museum of Fine Arts Carousel.｟newline｠｟newline｠4. Boston Museum of Science: This is a world-renowned science museum located in the heart of Boston. It features a variety of attractions, including the Boston Museum of Science Fountain, the Boston Museum of Science Bandstand, and the Boston Museum of Science Carousel.｟newline｠｟newline｠5. Boston Museum of History: This is a world-renowned history museum located in the heart of Boston` Installation instruction: Visit: https://github.com/OpenNMT/OpenNMT-py make sure you install flash-attn and autoawq Enjoy detailed MMLU scoring: ``` ACC-abstract_algebra: 0.3600 ACC-anatomy: 0.6444 ACC-astronomy: 0.7303 ACC-business_ethics: 0.6400 ACC-clinical_knowledge: 0.7283 ACC-college_biology: 0.8056 ACC-college_chemistry: 0.5300 ACC-college_computer_science: 0.5900 ACC-college_mathematics: 0.3700 ACC-college_medicine: 0.6936 ACC-college_physics: 0.4510 ACC-computer_security: 0.7900 ACC-conceptual_physics: 0.6468 ACC-econometrics: 0.5614 ACC-electrical_engineering: 0.6414 ACC-elementary_mathematics: 0.4630 ACC-formal_logic: 0.4524 ACC-global_facts: 0.4600 ACC-high_school_biology: 0.8000 ACC-high_school_chemistry: 0.5320 ACC-high_school_computer_science: 0.7400 ACC-high_school_european_history: 0.8121 ACC-high_school_geography: 0.8081 ACC-high_school_government_and_politics: 0.9275 ACC-high_school_macroeconomics: 0.6923 ACC-high_school_mathematics: 0.3667 ACC-high_school_microeconomics: 0.7731 ACC-high_school_physics: 0.4636 ACC-high_school_psychology: 0.8569 ACC-high_school_statistics: 0.5278 ACC-high_school_us_history: 0.8431 ACC-high_school_world_history: 0.8650 ACC-human_aging: 0.7175 ACC-human_sexuality: 0.7710 ACC-international_law: 0.8347 ACC-jurisprudence: 0.7778 ACC-logical_fallacies: 0.7791 ACC-machine_learning: 0.5357 ACC-management: 0.7767 ACC-marketing: 0.9145 ACC-medical_genetics: 0.7100 ACC-miscellaneous: 0.8404 ACC-moral_disputes: 0.7775 ACC-moral_scenarios: 0.4112 ACC-nutrition: 0.7876 ACC-philosophy: 0.7492 ACC-prehistory: 0.7963 ACC-professional_accounting: 0.5177 ACC-professional_law: 0.5111 ACC-professional_medicine: 0.7390 ACC-professional_psychology: 0.7304 ACC-public_relations: 0.6727 ACC-security_studies: 0.7061 ACC-sociology: 0.8706 ACC-us_foreign_policy: 0.9100 ACC-virology: 0.5060 ACC-world_religions: 0.8538 ACC-all: 0.6707 [2023-12-22 16:35:03,999 INFO] total run time 7156.16 ```

mahsamassoud/mnist-6

mahsamassoud

Entry not found

RUXHIR2828/laroi

RUXHIR2828

--- license: openrail ---

JugalOza/ReinforceCartpole1

JugalOza

reinforcement-learning

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: ReinforceCartpole1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 467.13 +/- 75.07 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

miweru/ochat3-5_schwurpus_merged

miweru

transformers

text-generation

Entry not found

ychordia/llama-2-7b-miniguanaco

ychordia

peft

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

spani/ArchDornan

spani

--- license: openrail ---

nemson/vicuna-7b-1.1

nemson

transformers

text-generation

--- license: llama2 --- This is a reupload

hkivancoral/smids_5x_deit_tiny_adamax_001_fold2

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: facebook/deit-small-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_tiny_adamax_001_fold2 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8968386023294509 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_5x_deit_tiny_adamax_001_fold2 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8883 - Accuracy: 0.8968 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3644 | 1.0 | 375 | 0.3398 | 0.8702 | | 0.2716 | 2.0 | 750 | 0.3172 | 0.8735 | | 0.3497 | 3.0 | 1125 | 0.3400 | 0.8586 | | 0.1669 | 4.0 | 1500 | 0.3794 | 0.8669 | | 0.2114 | 5.0 | 1875 | 0.2911 | 0.8902 | | 0.1067 | 6.0 | 2250 | 0.4133 | 0.8752 | | 0.1489 | 7.0 | 2625 | 0.5329 | 0.8419 | | 0.1233 | 8.0 | 3000 | 0.4750 | 0.8769 | | 0.121 | 9.0 | 3375 | 0.4209 | 0.8852 | | 0.0613 | 10.0 | 3750 | 0.3960 | 0.8918 | | 0.0185 | 11.0 | 4125 | 0.5647 | 0.8769 | | 0.07 | 12.0 | 4500 | 0.5185 | 0.8586 | | 0.0467 | 13.0 | 4875 | 0.5032 | 0.8985 | | 0.0041 | 14.0 | 5250 | 0.5742 | 0.8918 | | 0.0599 | 15.0 | 5625 | 0.7221 | 0.8652 | | 0.0363 | 16.0 | 6000 | 0.6853 | 0.8852 | | 0.0212 | 17.0 | 6375 | 0.5687 | 0.8985 | | 0.0007 | 18.0 | 6750 | 0.6790 | 0.8702 | | 0.0025 | 19.0 | 7125 | 0.5146 | 0.8935 | | 0.0511 | 20.0 | 7500 | 0.4949 | 0.9052 | | 0.0231 | 21.0 | 7875 | 0.5535 | 0.8952 | | 0.0 | 22.0 | 8250 | 0.7099 | 0.9002 | | 0.011 | 23.0 | 8625 | 0.7090 | 0.8902 | | 0.0118 | 24.0 | 9000 | 0.7009 | 0.9068 | | 0.0 | 25.0 | 9375 | 0.6598 | 0.8985 | | 0.0089 | 26.0 | 9750 | 0.7133 | 0.8902 | | 0.0142 | 27.0 | 10125 | 0.5886 | 0.9052 | | 0.0 | 28.0 | 10500 | 0.6881 | 0.9018 | | 0.0001 | 29.0 | 10875 | 0.7679 | 0.8985 | | 0.0001 | 30.0 | 11250 | 0.7339 | 0.8968 | | 0.0038 | 31.0 | 11625 | 0.8413 | 0.8918 | | 0.0044 | 32.0 | 12000 | 0.7669 | 0.9035 | | 0.0049 | 33.0 | 12375 | 0.7980 | 0.9052 | | 0.0 | 34.0 | 12750 | 0.7835 | 0.9035 | | 0.0 | 35.0 | 13125 | 0.8137 | 0.8968 | | 0.0 | 36.0 | 13500 | 0.8434 | 0.8968 | | 0.0 | 37.0 | 13875 | 0.8282 | 0.8952 | | 0.0 | 38.0 | 14250 | 0.8297 | 0.8968 | | 0.0 | 39.0 | 14625 | 0.8386 | 0.8935 | | 0.0034 | 40.0 | 15000 | 0.8364 | 0.8952 | | 0.0 | 41.0 | 15375 | 0.8624 | 0.8985 | | 0.0031 | 42.0 | 15750 | 0.8414 | 0.8968 | | 0.0026 | 43.0 | 16125 | 0.9010 | 0.8902 | | 0.0026 | 44.0 | 16500 | 0.8826 | 0.8952 | | 0.0029 | 45.0 | 16875 | 0.8702 | 0.8968 | | 0.0 | 46.0 | 17250 | 0.8727 | 0.8968 | | 0.0055 | 47.0 | 17625 | 0.8804 | 0.8968 | | 0.0 | 48.0 | 18000 | 0.8849 | 0.8968 | | 0.0025 | 49.0 | 18375 | 0.8877 | 0.8968 | | 0.0023 | 50.0 | 18750 | 0.8883 | 0.8968 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

trng1305/layoutlmv2-sroie-finetune

trng1305

transformers

Entry not found

Henoka/swin-base-patch4-window7-224-finetuned-lora-scenes

Henoka

peft

--- library_name: peft base_model: microsoft/swin-base-patch4-window7-224 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

dwang-LI/segformer-b0-finetuned-cityscapes-outputs

dwang-LI

Entry not found

Shiro836/RVC-Forsen

Shiro836

--- license: mit tags: - forsen - RVC - autism - ayaya ---

ADISH007/Aws_donut_10k_incremental_1_Epoch_12

ADISH007

transformers

Entry not found

sdadasfgdfgfdg/pacoca_turma_do_dudao_LuanKCT

sdadasfgdfgfdg

--- license: openrail ---

saikub/xslds-nsfw

saikub

--- license: mit ---

fshala/segformer_outputs

fshala

Entry not found

voxtell/voxtell

voxtell

Entry not found

GiusCat/tiffusion-mars-256

GiusCat

diffusers

Entry not found

hkivancoral/smids_3x_beit_base_sgd_0001_fold3

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_sgd_0001_fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7866666666666666 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_sgd_0001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5470 - Accuracy: 0.7867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.2271 | 1.0 | 225 | 1.2660 | 0.34 | | 1.1764 | 2.0 | 450 | 1.2039 | 0.36 | | 1.0866 | 3.0 | 675 | 1.1482 | 0.3833 | | 1.02 | 4.0 | 900 | 1.0954 | 0.41 | | 0.9521 | 5.0 | 1125 | 1.0436 | 0.4433 | | 0.9373 | 6.0 | 1350 | 0.9954 | 0.485 | | 0.8962 | 7.0 | 1575 | 0.9512 | 0.5317 | | 0.8694 | 8.0 | 1800 | 0.9106 | 0.5767 | | 0.8253 | 9.0 | 2025 | 0.8739 | 0.5967 | | 0.8297 | 10.0 | 2250 | 0.8416 | 0.635 | | 0.8158 | 11.0 | 2475 | 0.8130 | 0.6633 | | 0.75 | 12.0 | 2700 | 0.7869 | 0.685 | | 0.7851 | 13.0 | 2925 | 0.7633 | 0.69 | | 0.761 | 14.0 | 3150 | 0.7425 | 0.7017 | | 0.6927 | 15.0 | 3375 | 0.7233 | 0.7117 | | 0.7078 | 16.0 | 3600 | 0.7069 | 0.7217 | | 0.698 | 17.0 | 3825 | 0.6913 | 0.7283 | | 0.6847 | 18.0 | 4050 | 0.6778 | 0.7367 | | 0.6863 | 19.0 | 4275 | 0.6656 | 0.7383 | | 0.6396 | 20.0 | 4500 | 0.6548 | 0.7417 | | 0.6511 | 21.0 | 4725 | 0.6448 | 0.745 | | 0.6297 | 22.0 | 4950 | 0.6350 | 0.7517 | | 0.6013 | 23.0 | 5175 | 0.6267 | 0.755 | | 0.635 | 24.0 | 5400 | 0.6187 | 0.76 | | 0.6174 | 25.0 | 5625 | 0.6116 | 0.7583 | | 0.6201 | 26.0 | 5850 | 0.6053 | 0.7617 | | 0.5888 | 27.0 | 6075 | 0.5991 | 0.7617 | | 0.5833 | 28.0 | 6300 | 0.5934 | 0.7633 | | 0.6387 | 29.0 | 6525 | 0.5887 | 0.7683 | | 0.5339 | 30.0 | 6750 | 0.5839 | 0.7717 | | 0.5756 | 31.0 | 6975 | 0.5797 | 0.7767 | | 0.6386 | 32.0 | 7200 | 0.5758 | 0.775 | | 0.6245 | 33.0 | 7425 | 0.5722 | 0.775 | | 0.5779 | 34.0 | 7650 | 0.5690 | 0.7767 | | 0.57 | 35.0 | 7875 | 0.5661 | 0.7767 | | 0.5776 | 36.0 | 8100 | 0.5632 | 0.7767 | | 0.5861 | 37.0 | 8325 | 0.5611 | 0.7767 | | 0.5518 | 38.0 | 8550 | 0.5586 | 0.7767 | | 0.604 | 39.0 | 8775 | 0.5567 | 0.7817 | | 0.539 | 40.0 | 9000 | 0.5549 | 0.7833 | | 0.5457 | 41.0 | 9225 | 0.5534 | 0.7833 | | 0.6155 | 42.0 | 9450 | 0.5518 | 0.785 | | 0.5379 | 43.0 | 9675 | 0.5506 | 0.785 | | 0.5848 | 44.0 | 9900 | 0.5496 | 0.7867 | | 0.5814 | 45.0 | 10125 | 0.5488 | 0.7867 | | 0.5255 | 46.0 | 10350 | 0.5481 | 0.7867 | | 0.5726 | 47.0 | 10575 | 0.5476 | 0.7867 | | 0.5762 | 48.0 | 10800 | 0.5473 | 0.7867 | | 0.6192 | 49.0 | 11025 | 0.5471 | 0.7867 | | 0.5747 | 50.0 | 11250 | 0.5470 | 0.7867 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

platzi/platzi-vit-model-daniel-sanchez

platzi

transformers

image-classification

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: platzi-vit-model-daniel-sanchez results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9924812030075187 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # platzi-vit-model-daniel-sanchez This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0427 - Accuracy: 0.9925 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1488 | 3.85 | 500 | 0.0427 | 0.9925 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

Lol20232022/WizardLM-7B-uncensored-GPTQ

Lol20232022

Entry not found

hkivancoral/smids_3x_beit_base_rms_001_fold3

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_rms_001_fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7616666666666667 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_rms_001_fold3 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6251 - Accuracy: 0.7617 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.9148 | 1.0 | 225 | 0.9238 | 0.505 | | 0.8709 | 2.0 | 450 | 0.9060 | 0.515 | | 0.8398 | 3.0 | 675 | 0.8688 | 0.5317 | | 0.74 | 4.0 | 900 | 0.7859 | 0.5617 | | 0.7787 | 5.0 | 1125 | 0.7847 | 0.6017 | | 0.7532 | 6.0 | 1350 | 0.7702 | 0.63 | | 0.7432 | 7.0 | 1575 | 0.7450 | 0.655 | | 0.7264 | 8.0 | 1800 | 0.7610 | 0.6317 | | 0.7321 | 9.0 | 2025 | 0.7293 | 0.655 | | 0.6592 | 10.0 | 2250 | 0.7888 | 0.6367 | | 0.7528 | 11.0 | 2475 | 0.7158 | 0.6633 | | 0.7282 | 12.0 | 2700 | 0.7365 | 0.64 | | 0.6884 | 13.0 | 2925 | 0.6939 | 0.6733 | | 0.6852 | 14.0 | 3150 | 0.7006 | 0.67 | | 0.6011 | 15.0 | 3375 | 0.7591 | 0.6233 | | 0.6904 | 16.0 | 3600 | 0.6846 | 0.6717 | | 0.6393 | 17.0 | 3825 | 0.6741 | 0.7117 | | 0.6772 | 18.0 | 4050 | 0.6655 | 0.6683 | | 0.6409 | 19.0 | 4275 | 0.6658 | 0.6933 | | 0.5941 | 20.0 | 4500 | 0.6429 | 0.7017 | | 0.5753 | 21.0 | 4725 | 0.6753 | 0.6833 | | 0.5975 | 22.0 | 4950 | 0.6543 | 0.6917 | | 0.5954 | 23.0 | 5175 | 0.6358 | 0.7233 | | 0.5729 | 24.0 | 5400 | 0.6341 | 0.7133 | | 0.6313 | 25.0 | 5625 | 0.6336 | 0.7033 | | 0.5938 | 26.0 | 5850 | 0.6447 | 0.7083 | | 0.5183 | 27.0 | 6075 | 0.6247 | 0.7233 | | 0.5713 | 28.0 | 6300 | 0.6145 | 0.73 | | 0.5948 | 29.0 | 6525 | 0.5934 | 0.7317 | | 0.5273 | 30.0 | 6750 | 0.5971 | 0.7367 | | 0.5431 | 31.0 | 6975 | 0.5930 | 0.7433 | | 0.6025 | 32.0 | 7200 | 0.6434 | 0.7183 | | 0.5898 | 33.0 | 7425 | 0.5982 | 0.7383 | | 0.5455 | 34.0 | 7650 | 0.5983 | 0.75 | | 0.4857 | 35.0 | 7875 | 0.6162 | 0.735 | | 0.5822 | 36.0 | 8100 | 0.5546 | 0.7517 | | 0.4869 | 37.0 | 8325 | 0.5748 | 0.745 | | 0.4722 | 38.0 | 8550 | 0.5753 | 0.7417 | | 0.4982 | 39.0 | 8775 | 0.5694 | 0.7483 | | 0.4478 | 40.0 | 9000 | 0.5912 | 0.74 | | 0.4295 | 41.0 | 9225 | 0.5914 | 0.75 | | 0.4581 | 42.0 | 9450 | 0.5846 | 0.7617 | | 0.3797 | 43.0 | 9675 | 0.5733 | 0.7667 | | 0.4086 | 44.0 | 9900 | 0.6072 | 0.7517 | | 0.4164 | 45.0 | 10125 | 0.6033 | 0.7583 | | 0.3774 | 46.0 | 10350 | 0.6024 | 0.75 | | 0.392 | 47.0 | 10575 | 0.5976 | 0.7617 | | 0.3586 | 48.0 | 10800 | 0.6199 | 0.76 | | 0.3854 | 49.0 | 11025 | 0.6198 | 0.7667 | | 0.3586 | 50.0 | 11250 | 0.6251 | 0.7617 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

Efendi/bert-base-banking77-pt2

Efendi

Entry not found

bochu/vae

bochu

Entry not found

irfansss/lt_svm_model

irfansss

--- license: c-uda ---

sanghyo/FinalAssginment_model2

sanghyo

Entry not found

johnpaulbin/TorchMoji

johnpaulbin

Entry not found

markmongie/HanSoloEpVII

markmongie

--- license: mit ---

lawinsider/jina-embeddings-v2-small-en-quantized-arm64

lawinsider

transformers

fill-mask

Entry not found

lawinsider/jina-embeddings-v2-small-en-quantized-avx2

lawinsider

transformers

fill-mask

Entry not found

lawinsider/jina-embeddings-v2-small-en-quantized-avx512_vnni

lawinsider

transformers

fill-mask

Entry not found

lawinsider/jina-embeddings-v2-small-en

lawinsider

transformers

fill-mask

Entry not found

MatteoWood/llama-sexism-classifier

MatteoWood

Entry not found

otavinshow/karlvoz

otavinshow

--- license: openrail ---

fshala/1

fshala

Entry not found

star23/baller8

star23

transformers

text-generation

Invalid username or password.

Aksy/mistral-7b-chatbot

Aksy

Entry not found

gdurkin/segformer-b0-tiled-floods-S2-bri_grn_wet_pixel_values-Dec12-v2

gdurkin

transformers

Entry not found

hkivancoral/smids_5x_deit_tiny_adamax_001_fold3

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: facebook/deit-small-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_5x_deit_tiny_adamax_001_fold3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.905 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_5x_deit_tiny_adamax_001_fold3 This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0439 - Accuracy: 0.905 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3308 | 1.0 | 375 | 0.3353 | 0.875 | | 0.2337 | 2.0 | 750 | 0.3320 | 0.8817 | | 0.1696 | 3.0 | 1125 | 0.3479 | 0.8783 | | 0.1669 | 4.0 | 1500 | 0.3755 | 0.8767 | | 0.1864 | 5.0 | 1875 | 0.3099 | 0.8983 | | 0.1212 | 6.0 | 2250 | 0.3912 | 0.91 | | 0.1119 | 7.0 | 2625 | 0.4167 | 0.8817 | | 0.1024 | 8.0 | 3000 | 0.4153 | 0.8733 | | 0.0484 | 9.0 | 3375 | 0.5188 | 0.8733 | | 0.0551 | 10.0 | 3750 | 0.6042 | 0.885 | | 0.0468 | 11.0 | 4125 | 0.6570 | 0.8767 | | 0.0137 | 12.0 | 4500 | 0.6069 | 0.8733 | | 0.0177 | 13.0 | 4875 | 0.7091 | 0.8817 | | 0.0201 | 14.0 | 5250 | 0.7010 | 0.89 | | 0.0183 | 15.0 | 5625 | 0.6654 | 0.8867 | | 0.0149 | 16.0 | 6000 | 0.7079 | 0.8883 | | 0.0065 | 17.0 | 6375 | 0.6100 | 0.8933 | | 0.001 | 18.0 | 6750 | 0.9491 | 0.8817 | | 0.0034 | 19.0 | 7125 | 0.8269 | 0.8833 | | 0.0213 | 20.0 | 7500 | 0.8028 | 0.8833 | | 0.0137 | 21.0 | 7875 | 0.7227 | 0.8933 | | 0.0 | 22.0 | 8250 | 0.8796 | 0.8917 | | 0.0014 | 23.0 | 8625 | 0.8924 | 0.8733 | | 0.0002 | 24.0 | 9000 | 0.6942 | 0.8917 | | 0.0 | 25.0 | 9375 | 0.7445 | 0.89 | | 0.0 | 26.0 | 9750 | 0.7840 | 0.885 | | 0.0103 | 27.0 | 10125 | 0.7469 | 0.9033 | | 0.0 | 28.0 | 10500 | 0.8867 | 0.8783 | | 0.0 | 29.0 | 10875 | 0.8617 | 0.8867 | | 0.003 | 30.0 | 11250 | 0.8295 | 0.8983 | | 0.0008 | 31.0 | 11625 | 0.9061 | 0.895 | | 0.0 | 32.0 | 12000 | 0.8630 | 0.8967 | | 0.0 | 33.0 | 12375 | 0.8010 | 0.9017 | | 0.0 | 34.0 | 12750 | 0.8248 | 0.8983 | | 0.0 | 35.0 | 13125 | 0.8438 | 0.91 | | 0.0 | 36.0 | 13500 | 0.9235 | 0.9 | | 0.0 | 37.0 | 13875 | 0.8167 | 0.9083 | | 0.0 | 38.0 | 14250 | 0.8531 | 0.9033 | | 0.0 | 39.0 | 14625 | 0.9035 | 0.9067 | | 0.0027 | 40.0 | 15000 | 0.9614 | 0.9017 | | 0.0 | 41.0 | 15375 | 0.9740 | 0.9017 | | 0.0 | 42.0 | 15750 | 0.9907 | 0.9 | | 0.0 | 43.0 | 16125 | 0.9964 | 0.9033 | | 0.0 | 44.0 | 16500 | 1.0084 | 0.9033 | | 0.0 | 45.0 | 16875 | 1.0215 | 0.905 | | 0.0 | 46.0 | 17250 | 1.0234 | 0.9017 | | 0.0 | 47.0 | 17625 | 1.0315 | 0.905 | | 0.0 | 48.0 | 18000 | 1.0372 | 0.905 | | 0.0 | 49.0 | 18375 | 1.0413 | 0.905 | | 0.0 | 50.0 | 18750 | 1.0439 | 0.905 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.1+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

trng1305/layoutlmv2-sroie-finetunev1

trng1305

transformers

--- tags: - generated_from_trainer model-index: - name: layoutlmv2-sroie-finetunev1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlmv2-sroie-finetunev1 This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1271 - Address: {'precision': 0.9885554425228891, 'recall': 0.9948809828512926, 'f1': 0.9917081260364842, 'number': 3907} - Company: {'precision': 0.974934036939314, 'recall': 0.9912810194500336, 'f1': 0.9830395743265714, 'number': 1491} - Date: {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} - Total: {'precision': 0.8826666666666667, 'recall': 0.8921832884097035, 'f1': 0.8873994638069707, 'number': 371} - Overall Precision: 0.9794 - Overall Recall: 0.9873 - Overall F1: 0.9833 - Overall Accuracy: 0.9949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - label_smoothing_factor: 0.02 ### Training results | Training Loss | Epoch | Step | Validation Loss | Address | Company | Date | Total | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2409 | 1.0 | 40 | 0.1537 | {'precision': 0.9862804878048781, 'recall': 0.9936012285641157, 'f1': 0.9899273237281654, 'number': 3907} | {'precision': 0.908923076923077, 'recall': 0.9906103286384976, 'f1': 0.94801026957638, 'number': 1491} | {'precision': 0.9414414414414415, 'recall': 0.9766355140186916, 'f1': 0.9587155963302753, 'number': 428} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 371} | 0.9620 | 0.9322 | 0.9469 | 0.9843 | | 0.1402 | 2.0 | 80 | 0.1343 | {'precision': 0.9860476915271436, 'recall': 0.9948809828512926, 'f1': 0.9904446426296343, 'number': 3907} | {'precision': 0.946257197696737, 'recall': 0.9919517102615694, 'f1': 0.9685658153241651, 'number': 1491} | {'precision': 0.9813519813519813, 'recall': 0.9836448598130841, 'f1': 0.9824970828471412, 'number': 428} | {'precision': 0.6899038461538461, 'recall': 0.7735849056603774, 'f1': 0.7293519695044473, 'number': 371} | 0.9565 | 0.9802 | 0.9682 | 0.9903 | | 0.1259 | 3.0 | 120 | 0.1262 | {'precision': 0.9918200408997955, 'recall': 0.9930893268492449, 'f1': 0.9924542780406702, 'number': 3907} | {'precision': 0.9800266311584553, 'recall': 0.9872568745808182, 'f1': 0.9836284664216505, 'number': 1491} | {'precision': 0.9928741092636579, 'recall': 0.9766355140186916, 'f1': 0.9846878680800941, 'number': 428} | {'precision': 0.819672131147541, 'recall': 0.8086253369272237, 'f1': 0.814111261872456, 'number': 371} | 0.9789 | 0.9795 | 0.9792 | 0.9937 | | 0.1198 | 4.0 | 160 | 0.1245 | {'precision': 0.9913309535951046, 'recall': 0.9951369337087279, 'f1': 0.9932302976114447, 'number': 3907} | {'precision': 0.9774535809018567, 'recall': 0.98859825620389, 'f1': 0.9829943314438147, 'number': 1491} | {'precision': 0.997624703087886, 'recall': 0.9813084112149533, 'f1': 0.9893992932862191, 'number': 428} | {'precision': 0.7985257985257985, 'recall': 0.876010781671159, 'f1': 0.8354755784061697, 'number': 371} | 0.9759 | 0.9855 | 0.9807 | 0.9941 | | 0.1168 | 5.0 | 200 | 0.1249 | {'precision': 0.9918242207460398, 'recall': 0.9936012285641157, 'f1': 0.9927119294207902, 'number': 3907} | {'precision': 0.9679319371727748, 'recall': 0.9919517102615694, 'f1': 0.9797946339847631, 'number': 1491} | {'precision': 0.990632318501171, 'recall': 0.9883177570093458, 'f1': 0.9894736842105264, 'number': 428} | {'precision': 0.8372093023255814, 'recall': 0.8733153638814016, 'f1': 0.8548812664907651, 'number': 371} | 0.9763 | 0.9856 | 0.9810 | 0.9943 | | 0.1142 | 6.0 | 240 | 0.1250 | {'precision': 0.9923175416133163, 'recall': 0.991809572562068, 'f1': 0.9920634920634921, 'number': 3907} | {'precision': 0.9813581890812251, 'recall': 0.98859825620389, 'f1': 0.9849649181423321, 'number': 1491} | {'precision': 1.0, 'recall': 0.9813084112149533, 'f1': 0.9905660377358491, 'number': 428} | {'precision': 0.8802228412256268, 'recall': 0.8517520215633423, 'f1': 0.8657534246575341, 'number': 371} | 0.9837 | 0.9819 | 0.9828 | 0.9948 | | 0.113 | 7.0 | 280 | 0.1244 | {'precision': 0.9908139831589691, 'recall': 0.993857179421551, 'f1': 0.9923332481472016, 'number': 3907} | {'precision': 0.9788079470198675, 'recall': 0.9912810194500336, 'f1': 0.9850049983338888, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428} | {'precision': 0.9054441260744985, 'recall': 0.8517520215633423, 'f1': 0.8777777777777778, 'number': 371} | 0.9837 | 0.9842 | 0.9839 | 0.9952 | | 0.112 | 8.0 | 320 | 0.1259 | {'precision': 0.988552531162554, 'recall': 0.9946250319938572, 'f1': 0.9915794845623884, 'number': 3907} | {'precision': 0.9730617608409987, 'recall': 0.9932930918846412, 'f1': 0.9830733488217724, 'number': 1491} | {'precision': 1.0, 'recall': 0.9836448598130841, 'f1': 0.9917550058892814, 'number': 428} | {'precision': 0.8663101604278075, 'recall': 0.8733153638814016, 'f1': 0.8697986577181209, 'number': 371} | 0.9782 | 0.9863 | 0.9822 | 0.9946 | | 0.1105 | 9.0 | 360 | 0.1262 | {'precision': 0.9880559085133418, 'recall': 0.9951369337087279, 'f1': 0.991583779648049, 'number': 3907} | {'precision': 0.9788219722038385, 'recall': 0.9919517102615694, 'f1': 0.9853431045969354, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428} | {'precision': 0.8895027624309392, 'recall': 0.8679245283018868, 'f1': 0.878581173260573, 'number': 371} | 0.9809 | 0.9861 | 0.9835 | 0.9950 | | 0.1102 | 10.0 | 400 | 0.1258 | {'precision': 0.9905346635968278, 'recall': 0.991041719989762, 'f1': 0.9907881269191403, 'number': 3907} | {'precision': 0.9710716633793557, 'recall': 0.9906103286384976, 'f1': 0.9807436918990704, 'number': 1491} | {'precision': 0.9976415094339622, 'recall': 0.9883177570093458, 'f1': 0.9929577464788731, 'number': 428} | {'precision': 0.8605263157894737, 'recall': 0.8814016172506739, 'f1': 0.8708388814913449, 'number': 371} | 0.9783 | 0.9842 | 0.9813 | 0.9945 | | 0.1091 | 11.0 | 440 | 0.1263 | {'precision': 0.990316004077472, 'recall': 0.9946250319938572, 'f1': 0.9924658408887755, 'number': 3907} | {'precision': 0.9724409448818898, 'recall': 0.993963782696177, 'f1': 0.9830845771144279, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428} | {'precision': 0.8491048593350383, 'recall': 0.894878706199461, 'f1': 0.8713910761154856, 'number': 371} | 0.9778 | 0.9879 | 0.9828 | 0.9948 | | 0.1092 | 12.0 | 480 | 0.1277 | {'precision': 0.9885437881873728, 'recall': 0.993857179421551, 'f1': 0.991193363114231, 'number': 3907} | {'precision': 0.965472312703583, 'recall': 0.993963782696177, 'f1': 0.9795109054857898, 'number': 1491} | {'precision': 0.9976359338061466, 'recall': 0.985981308411215, 'f1': 0.991774383078731, 'number': 428} | {'precision': 0.8907103825136612, 'recall': 0.8787061994609164, 'f1': 0.8846675712347355, 'number': 371} | 0.9778 | 0.9864 | 0.9821 | 0.9946 | | 0.1082 | 13.0 | 520 | 0.1271 | {'precision': 0.9890501655207538, 'recall': 0.9941131302789864, 'f1': 0.9915751850906306, 'number': 3907} | {'precision': 0.9794019933554817, 'recall': 0.98859825620389, 'f1': 0.9839786381842456, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} | {'precision': 0.8477157360406091, 'recall': 0.9002695417789758, 'f1': 0.8732026143790849, 'number': 371} | 0.9782 | 0.9866 | 0.9824 | 0.9947 | | 0.1079 | 14.0 | 560 | 0.1274 | {'precision': 0.9888040712468193, 'recall': 0.9946250319938572, 'f1': 0.991706009952788, 'number': 3907} | {'precision': 0.974934036939314, 'recall': 0.9912810194500336, 'f1': 0.9830395743265714, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} | {'precision': 0.8691099476439791, 'recall': 0.894878706199461, 'f1': 0.8818061088977424, 'number': 371} | 0.9786 | 0.9873 | 0.9829 | 0.9948 | | 0.1076 | 15.0 | 600 | 0.1268 | {'precision': 0.9887983706720977, 'recall': 0.9941131302789864, 'f1': 0.9914486279514996, 'number': 3907} | {'precision': 0.9749009247027741, 'recall': 0.9899396378269618, 'f1': 0.9823627287853578, 'number': 1491} | {'precision': 0.9976359338061466, 'recall': 0.985981308411215, 'f1': 0.991774383078731, 'number': 428} | {'precision': 0.8840970350404312, 'recall': 0.8840970350404312, 'f1': 0.8840970350404312, 'number': 371} | 0.9798 | 0.9860 | 0.9829 | 0.9948 | | 0.1076 | 16.0 | 640 | 0.1268 | {'precision': 0.988552531162554, 'recall': 0.9946250319938572, 'f1': 0.9915794845623884, 'number': 3907} | {'precision': 0.97556142668428, 'recall': 0.9906103286384976, 'f1': 0.9830282861896837, 'number': 1491} | {'precision': 0.9976359338061466, 'recall': 0.985981308411215, 'f1': 0.991774383078731, 'number': 428} | {'precision': 0.8934426229508197, 'recall': 0.8814016172506739, 'f1': 0.8873812754409769, 'number': 371} | 0.9804 | 0.9863 | 0.9833 | 0.9949 | | 0.1073 | 17.0 | 680 | 0.1268 | {'precision': 0.9895541401273885, 'recall': 0.9941131302789864, 'f1': 0.9918283963227783, 'number': 3907} | {'precision': 0.974917491749175, 'recall': 0.9906103286384976, 'f1': 0.9827012641383899, 'number': 1491} | {'precision': 0.9976359338061466, 'recall': 0.985981308411215, 'f1': 0.991774383078731, 'number': 428} | {'precision': 0.8921832884097035, 'recall': 0.8921832884097035, 'f1': 0.8921832884097035, 'number': 371} | 0.9808 | 0.9866 | 0.9837 | 0.9950 | | 0.1071 | 18.0 | 720 | 0.1265 | {'precision': 0.9895568008150789, 'recall': 0.9943690811364219, 'f1': 0.9919571045576407, 'number': 3907} | {'precision': 0.9761904761904762, 'recall': 0.9899396378269618, 'f1': 0.983016983016983, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} | {'precision': 0.8873994638069705, 'recall': 0.8921832884097035, 'f1': 0.8897849462365591, 'number': 371} | 0.9806 | 0.9866 | 0.9836 | 0.9950 | | 0.1072 | 19.0 | 760 | 0.1271 | {'precision': 0.9885554425228891, 'recall': 0.9948809828512926, 'f1': 0.9917081260364842, 'number': 3907} | {'precision': 0.974934036939314, 'recall': 0.9912810194500336, 'f1': 0.9830395743265714, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} | {'precision': 0.8756613756613757, 'recall': 0.8921832884097035, 'f1': 0.8838451268357811, 'number': 371} | 0.9789 | 0.9873 | 0.9830 | 0.9948 | | 0.1072 | 20.0 | 800 | 0.1271 | {'precision': 0.9885554425228891, 'recall': 0.9948809828512926, 'f1': 0.9917081260364842, 'number': 3907} | {'precision': 0.974934036939314, 'recall': 0.9912810194500336, 'f1': 0.9830395743265714, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} | {'precision': 0.8826666666666667, 'recall': 0.8921832884097035, 'f1': 0.8873994638069707, 'number': 371} | 0.9794 | 0.9873 | 0.9833 | 0.9949 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.13.3

hamzaahmedkhater25/JohnWick5_Reckoning

hamzaahmedkhater25

Entry not found

sayhamza/LLM_Model_SayedHamza

sayhamza

--- license: apache-2.0 ---

fshala/segformer-cloud

fshala

transformers

image-segmentation

--- license: other base_model: nvidia/mit-b0 tags: - image-segmentation - vision - generated_from_trainer model-index: - name: segformer-cloud results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-cloud This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

hkivancoral/smids_3x_beit_base_sgd_0001_fold4

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_sgd_0001_fold4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.77 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_sgd_0001_fold4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5543 - Accuracy: 0.77 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.2016 | 1.0 | 225 | 1.2841 | 0.345 | | 1.1719 | 2.0 | 450 | 1.2211 | 0.3617 | | 1.0758 | 3.0 | 675 | 1.1630 | 0.3733 | | 1.0147 | 4.0 | 900 | 1.1086 | 0.4033 | | 1.0074 | 5.0 | 1125 | 1.0560 | 0.4317 | | 0.9405 | 6.0 | 1350 | 1.0063 | 0.4617 | | 0.9199 | 7.0 | 1575 | 0.9602 | 0.51 | | 0.9125 | 8.0 | 1800 | 0.9177 | 0.5617 | | 0.8654 | 9.0 | 2025 | 0.8771 | 0.6017 | | 0.8229 | 10.0 | 2250 | 0.8432 | 0.6333 | | 0.8209 | 11.0 | 2475 | 0.8129 | 0.6567 | | 0.775 | 12.0 | 2700 | 0.7860 | 0.675 | | 0.7435 | 13.0 | 2925 | 0.7620 | 0.6883 | | 0.7034 | 14.0 | 3150 | 0.7408 | 0.695 | | 0.7434 | 15.0 | 3375 | 0.7223 | 0.7033 | | 0.7412 | 16.0 | 3600 | 0.7055 | 0.7133 | | 0.6871 | 17.0 | 3825 | 0.6906 | 0.7167 | | 0.6997 | 18.0 | 4050 | 0.6769 | 0.725 | | 0.6998 | 19.0 | 4275 | 0.6646 | 0.7267 | | 0.6623 | 20.0 | 4500 | 0.6540 | 0.7283 | | 0.668 | 21.0 | 4725 | 0.6441 | 0.73 | | 0.6697 | 22.0 | 4950 | 0.6349 | 0.7317 | | 0.6394 | 23.0 | 5175 | 0.6268 | 0.7383 | | 0.6267 | 24.0 | 5400 | 0.6193 | 0.7383 | | 0.6154 | 25.0 | 5625 | 0.6125 | 0.7433 | | 0.5813 | 26.0 | 5850 | 0.6070 | 0.745 | | 0.612 | 27.0 | 6075 | 0.6014 | 0.7483 | | 0.6011 | 28.0 | 6300 | 0.5964 | 0.7483 | | 0.5913 | 29.0 | 6525 | 0.5915 | 0.7517 | | 0.5609 | 30.0 | 6750 | 0.5872 | 0.76 | | 0.5861 | 31.0 | 6975 | 0.5835 | 0.7617 | | 0.5483 | 32.0 | 7200 | 0.5800 | 0.76 | | 0.5986 | 33.0 | 7425 | 0.5766 | 0.7633 | | 0.619 | 34.0 | 7650 | 0.5736 | 0.7617 | | 0.5813 | 35.0 | 7875 | 0.5710 | 0.765 | | 0.6084 | 36.0 | 8100 | 0.5683 | 0.7667 | | 0.6052 | 37.0 | 8325 | 0.5664 | 0.765 | | 0.5601 | 38.0 | 8550 | 0.5646 | 0.765 | | 0.5878 | 39.0 | 8775 | 0.5631 | 0.7633 | | 0.6072 | 40.0 | 9000 | 0.5616 | 0.7633 | | 0.5597 | 41.0 | 9225 | 0.5601 | 0.7683 | | 0.5694 | 42.0 | 9450 | 0.5588 | 0.7667 | | 0.5553 | 43.0 | 9675 | 0.5575 | 0.77 | | 0.5942 | 44.0 | 9900 | 0.5566 | 0.77 | | 0.6005 | 45.0 | 10125 | 0.5559 | 0.77 | | 0.58 | 46.0 | 10350 | 0.5553 | 0.77 | | 0.5814 | 47.0 | 10575 | 0.5548 | 0.77 | | 0.5609 | 48.0 | 10800 | 0.5545 | 0.7717 | | 0.6076 | 49.0 | 11025 | 0.5543 | 0.77 | | 0.5819 | 50.0 | 11250 | 0.5543 | 0.77 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

EthanRhys/Flannery-Masters-EX

EthanRhys

--- license: openrail ---

behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError1.0_Seed102

behzadnet

peft

--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0

hkivancoral/smids_3x_beit_base_rms_001_fold4

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_rms_001_fold4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7583333333333333 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_rms_001_fold4 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6701 - Accuracy: 0.7583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.1325 | 1.0 | 225 | 1.0820 | 0.33 | | 0.9647 | 2.0 | 450 | 0.8610 | 0.5233 | | 0.9155 | 3.0 | 675 | 0.8470 | 0.5233 | | 0.8045 | 4.0 | 900 | 0.7955 | 0.5633 | | 0.9422 | 5.0 | 1125 | 0.7622 | 0.5833 | | 0.7846 | 6.0 | 1350 | 0.7519 | 0.6167 | | 0.7593 | 7.0 | 1575 | 0.7344 | 0.6267 | | 0.7843 | 8.0 | 1800 | 0.7233 | 0.625 | | 0.758 | 9.0 | 2025 | 0.6963 | 0.675 | | 0.7521 | 10.0 | 2250 | 0.7172 | 0.6367 | | 0.7273 | 11.0 | 2475 | 0.7162 | 0.6867 | | 0.7253 | 12.0 | 2700 | 0.7548 | 0.6367 | | 0.7429 | 13.0 | 2925 | 0.7073 | 0.6933 | | 0.6572 | 14.0 | 3150 | 0.7052 | 0.6733 | | 0.668 | 15.0 | 3375 | 0.6850 | 0.6967 | | 0.7304 | 16.0 | 3600 | 0.6940 | 0.6633 | | 0.6361 | 17.0 | 3825 | 0.7269 | 0.68 | | 0.7538 | 18.0 | 4050 | 0.6743 | 0.7 | | 0.7884 | 19.0 | 4275 | 0.6564 | 0.7067 | | 0.6141 | 20.0 | 4500 | 0.7026 | 0.68 | | 0.6658 | 21.0 | 4725 | 0.6553 | 0.6983 | | 0.7013 | 22.0 | 4950 | 0.6518 | 0.7133 | | 0.6988 | 23.0 | 5175 | 0.7048 | 0.6433 | | 0.6506 | 24.0 | 5400 | 0.6539 | 0.725 | | 0.6644 | 25.0 | 5625 | 0.6442 | 0.7083 | | 0.6782 | 26.0 | 5850 | 0.6333 | 0.735 | | 0.6752 | 27.0 | 6075 | 0.6258 | 0.72 | | 0.7055 | 28.0 | 6300 | 0.6242 | 0.7267 | | 0.6118 | 29.0 | 6525 | 0.6321 | 0.7333 | | 0.6455 | 30.0 | 6750 | 0.6581 | 0.7067 | | 0.5483 | 31.0 | 6975 | 0.6054 | 0.745 | | 0.6021 | 32.0 | 7200 | 0.6170 | 0.7333 | | 0.5857 | 33.0 | 7425 | 0.6206 | 0.7367 | | 0.657 | 34.0 | 7650 | 0.6354 | 0.72 | | 0.6083 | 35.0 | 7875 | 0.6084 | 0.7517 | | 0.6036 | 36.0 | 8100 | 0.6122 | 0.7267 | | 0.5986 | 37.0 | 8325 | 0.6097 | 0.7383 | | 0.5126 | 38.0 | 8550 | 0.6043 | 0.7467 | | 0.5361 | 39.0 | 8775 | 0.6148 | 0.7483 | | 0.5689 | 40.0 | 9000 | 0.6233 | 0.7567 | | 0.5001 | 41.0 | 9225 | 0.6245 | 0.7567 | | 0.5505 | 42.0 | 9450 | 0.6430 | 0.745 | | 0.5115 | 43.0 | 9675 | 0.6524 | 0.7333 | | 0.5425 | 44.0 | 9900 | 0.6414 | 0.7467 | | 0.5416 | 45.0 | 10125 | 0.6407 | 0.75 | | 0.4698 | 46.0 | 10350 | 0.6413 | 0.7367 | | 0.5037 | 47.0 | 10575 | 0.6665 | 0.7533 | | 0.5074 | 48.0 | 10800 | 0.6614 | 0.7583 | | 0.4187 | 49.0 | 11025 | 0.6632 | 0.755 | | 0.4669 | 50.0 | 11250 | 0.6701 | 0.7583 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError1.0_Seed102

behzadnet

peft

--- library_name: peft base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.7.0.dev0

praiseneh/lora_model_weights_r_16_epochs_10_batch_size_4_gradient_steps_4_lr_0.001_warmup_100

praiseneh

Entry not found

agailloty/houseprice

agailloty

Entry not found

marielandryceo/ScientificAI

marielandryceo

--- license: unknown ---

Claire-codes/llama2-autoTrain-alpaca-gpt-4

Claire-codes

Entry not found

praiseneh/lora_model_weights_r_16_epochs_20_batch_size_4_gradient_steps_4_lr_0.001_warmup_100

praiseneh

Entry not found

StefanMachine/TeachingBud

StefanMachine

--- license: llama2 ---

yosthin06/whisper-tiny_yosthingalindo

yosthin06

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny_yosthingalindo results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 metrics: - name: Wer type: wer value: 0.33530106257378983 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny_yosthingalindo This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.5824 - Wer Ortho: 0.3424 - Wer: 0.3353 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 250 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.3677 | 1.72 | 50 | 0.5198 | 0.3849 | 0.3648 | | 0.1925 | 3.45 | 100 | 0.5038 | 0.3671 | 0.3518 | | 0.0836 | 5.17 | 150 | 0.5206 | 0.3547 | 0.3406 | | 0.0265 | 6.9 | 200 | 0.5520 | 0.3627 | 0.3518 | | 0.008 | 8.62 | 250 | 0.5824 | 0.3424 | 0.3353 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.13.3

prajwalJumde/classification_13Dec

prajwalJumde

Entry not found

faisaltareque/BengaliByteLevelBPETokenizerFast

faisaltareque

Entry not found

ThuyNT03/KLTN_COQE_viT5_MvP_v1

ThuyNT03

transformers

text2text-generation

--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: KLTN_COQE_viT5_MvP_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # KLTN_COQE_viT5_MvP_v1 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1

AigizK/wav2vec2-large-mms-1b-tatar

AigizK

transformers

automatic-speech-recognition

--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-mms-1b-tatar results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-mms-1b-tatar This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1884 - Wer: 0.1618 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 100000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:------:|:---------------:|:------:| | 3.734 | 0.0 | 100 | 0.3737 | 0.3767 | | 0.415 | 0.0 | 200 | 0.3114 | 0.2868 | | 0.3977 | 0.0 | 300 | 0.2933 | 0.2682 | | 0.374 | 0.0 | 400 | 0.3043 | 0.2897 | | 0.37 | 0.01 | 500 | 0.3074 | 0.2717 | | 0.3503 | 0.01 | 600 | 0.3097 | 0.2818 | | 0.3765 | 0.01 | 700 | 0.2897 | 0.2739 | | 0.3411 | 0.01 | 800 | 0.2865 | 0.2660 | | 0.3448 | 0.01 | 900 | 0.2885 | 0.2509 | | 0.3363 | 0.01 | 1000 | 0.2857 | 0.2538 | | 0.3445 | 0.01 | 1100 | 0.2767 | 0.2451 | | 0.2992 | 0.01 | 1200 | 0.2881 | 0.2509 | | 0.311 | 0.01 | 1300 | 0.3517 | 0.2401 | | 0.2884 | 0.01 | 1400 | 0.3339 | 0.2495 | | 0.3027 | 0.01 | 1500 | 0.3485 | 0.2595 | | 0.2891 | 0.02 | 1600 | 0.3452 | 0.2574 | | 0.2702 | 0.02 | 1700 | 0.3474 | 0.2588 | | 0.2754 | 0.02 | 1800 | 0.3471 | 0.2437 | | 0.265 | 0.02 | 1900 | 0.3507 | 0.2459 | | 0.274 | 0.02 | 2000 | 0.3546 | 0.2365 | | 0.2792 | 0.02 | 2100 | 0.3641 | 0.2509 | | 0.2648 | 0.02 | 2200 | 0.3623 | 0.2265 | | 0.2668 | 0.02 | 2300 | 0.3299 | 0.2315 | | 0.2615 | 0.02 | 2400 | 0.3750 | 0.2408 | | 0.2774 | 0.03 | 2500 | 0.3363 | 0.2365 | | 0.2627 | 0.03 | 2600 | 0.3280 | 0.2315 | | 0.264 | 0.03 | 2700 | 0.3240 | 0.2315 | | 0.2634 | 0.03 | 2800 | 0.3512 | 0.2236 | | 0.2745 | 0.03 | 2900 | 0.3326 | 0.2265 | | 0.2787 | 0.03 | 3000 | 0.3194 | 0.2358 | | 0.2654 | 0.03 | 3100 | 0.3238 | 0.2322 | | 0.2704 | 0.03 | 3200 | 0.3342 | 0.2351 | | 0.2599 | 0.03 | 3300 | 0.3518 | 0.2387 | | 0.2477 | 0.03 | 3400 | 0.3258 | 0.2301 | | 0.2597 | 0.04 | 3500 | 0.3151 | 0.2344 | | 0.2582 | 0.04 | 3600 | 0.3250 | 0.2315 | | 0.2563 | 0.04 | 3700 | 0.3322 | 0.2344 | | 0.269 | 0.04 | 3800 | 0.3218 | 0.2416 | | 0.2572 | 0.04 | 3900 | 0.3196 | 0.2308 | | 0.2683 | 0.04 | 4000 | 0.3497 | 0.2365 | | 0.2542 | 0.04 | 4100 | 0.3290 | 0.2466 | | 0.2545 | 0.04 | 4200 | 0.3238 | 0.2437 | | 0.2684 | 0.04 | 4300 | 0.3131 | 0.2221 | | 0.2518 | 0.04 | 4400 | 0.3267 | 0.2286 | | 0.2405 | 0.04 | 4500 | 0.3354 | 0.2243 | | 0.2657 | 0.05 | 4600 | 0.3380 | 0.2351 | | 0.2658 | 0.05 | 4700 | 0.3372 | 0.2437 | | 0.2497 | 0.05 | 4800 | 0.3475 | 0.2286 | | 0.256 | 0.05 | 4900 | 0.3447 | 0.2401 | | 0.2631 | 0.05 | 5000 | 0.2976 | 0.2416 | | 0.2708 | 0.05 | 5100 | 0.3358 | 0.2344 | | 0.2676 | 0.05 | 5200 | 0.3251 | 0.2394 | | 0.2523 | 0.05 | 5300 | 0.3347 | 0.2315 | | 0.248 | 0.05 | 5400 | 0.3308 | 0.2315 | | 0.2284 | 0.06 | 5500 | 0.3338 | 0.2372 | | 0.2504 | 0.06 | 5600 | 0.3475 | 0.2308 | | 0.2531 | 0.06 | 5700 | 0.3227 | 0.2336 | | 0.2544 | 0.06 | 5800 | 0.3184 | 0.2315 | | 0.2537 | 0.06 | 5900 | 0.3083 | 0.2265 | | 0.257 | 0.06 | 6000 | 0.3173 | 0.2308 | | 0.2531 | 0.06 | 6100 | 0.3298 | 0.2322 | | 0.2497 | 0.06 | 6200 | 0.3001 | 0.2250 | | 0.2602 | 0.06 | 6300 | 0.3262 | 0.2301 | | 0.2341 | 0.06 | 6400 | 0.3273 | 0.2229 | | 0.2521 | 0.07 | 6500 | 0.3334 | 0.2286 | | 0.2597 | 0.07 | 6600 | 0.3171 | 0.2157 | | 0.2496 | 0.07 | 6700 | 0.3340 | 0.2243 | | 0.2412 | 0.07 | 6800 | 0.3035 | 0.2157 | | 0.2478 | 0.07 | 6900 | 0.3274 | 0.2171 | | 0.2494 | 0.07 | 7000 | 0.3218 | 0.2250 | | 0.2525 | 0.07 | 7100 | 0.3321 | 0.2265 | | 0.2537 | 0.07 | 7200 | 0.3262 | 0.2265 | | 0.2509 | 0.07 | 7300 | 0.3286 | 0.2322 | | 0.2434 | 0.07 | 7400 | 0.3259 | 0.2193 | | 0.2394 | 0.07 | 7500 | 0.3303 | 0.2185 | | 0.2527 | 0.08 | 7600 | 0.3237 | 0.2229 | | 0.3968 | 0.08 | 7700 | 0.2598 | 0.2315 | | 0.3999 | 0.08 | 7800 | 0.2541 | 0.2272 | | 0.3684 | 0.08 | 7900 | 0.2858 | 0.2351 | | 0.3622 | 0.08 | 8000 | 0.2591 | 0.2214 | | 0.3823 | 0.08 | 8100 | 0.2529 | 0.2250 | | 0.3638 | 0.08 | 8200 | 0.2598 | 0.2301 | | 0.4044 | 0.08 | 8300 | 0.2586 | 0.2214 | | 0.3785 | 0.08 | 8400 | 0.2464 | 0.2265 | | 0.3412 | 0.09 | 8500 | 0.2611 | 0.2214 | | 0.3626 | 0.09 | 8600 | 0.2428 | 0.2193 | | 0.3571 | 0.09 | 8700 | 0.2369 | 0.2121 | | 0.3877 | 0.09 | 8800 | 0.2461 | 0.2243 | | 0.3797 | 0.09 | 8900 | 0.2574 | 0.2272 | | 0.3454 | 0.09 | 9000 | 0.2653 | 0.2135 | | 0.347 | 0.09 | 9100 | 0.2584 | 0.2265 | | 0.3592 | 0.09 | 9200 | 0.2495 | 0.2250 | | 0.3536 | 0.09 | 9300 | 0.2490 | 0.2207 | | 0.3655 | 0.09 | 9400 | 0.2447 | 0.2214 | | 0.3668 | 0.1 | 9500 | 0.2423 | 0.2078 | | 0.3491 | 0.1 | 9600 | 0.2456 | 0.2164 | | 0.3442 | 0.1 | 9700 | 0.2411 | 0.2027 | | 0.372 | 0.1 | 9800 | 0.2618 | 0.2193 | | 0.3345 | 0.1 | 9900 | 0.2500 | 0.2272 | | 0.3628 | 0.1 | 10000 | 0.2438 | 0.2128 | | 0.3781 | 0.1 | 10100 | 0.2546 | 0.2200 | | 0.3478 | 0.1 | 10200 | 0.2553 | 0.2157 | | 0.3461 | 0.1 | 10300 | 0.2543 | 0.2128 | | 0.35 | 0.1 | 10400 | 0.2418 | 0.2121 | | 0.3557 | 0.1 | 10500 | 0.2628 | 0.2207 | | 0.3384 | 0.11 | 10600 | 0.2396 | 0.2171 | | 0.3373 | 0.11 | 10700 | 0.2582 | 0.2200 | | 0.3596 | 0.11 | 10800 | 0.2554 | 0.2092 | | 0.3218 | 0.11 | 10900 | 0.2389 | 0.2178 | | 0.3532 | 0.11 | 11000 | 0.2454 | 0.2279 | | 0.3661 | 0.11 | 11100 | 0.2455 | 0.2250 | | 0.362 | 0.11 | 11200 | 0.2461 | 0.2193 | | 0.3465 | 0.11 | 11300 | 0.2437 | 0.2243 | | 0.3345 | 0.11 | 11400 | 0.2427 | 0.2085 | | 0.3691 | 0.12 | 11500 | 0.2488 | 0.2221 | | 0.3386 | 0.12 | 11600 | 0.2476 | 0.2135 | | 0.3425 | 0.12 | 11700 | 0.2449 | 0.2243 | | 0.345 | 0.12 | 11800 | 0.2480 | 0.2135 | | 0.3426 | 0.12 | 11900 | 0.2587 | 0.2272 | | 0.3234 | 0.12 | 12000 | 0.2393 | 0.2200 | | 0.3402 | 0.12 | 12100 | 0.2471 | 0.2185 | | 0.3225 | 0.12 | 12200 | 0.2551 | 0.2157 | | 0.3503 | 0.12 | 12300 | 0.2539 | 0.2243 | | 0.3396 | 0.12 | 12400 | 0.2596 | 0.2236 | | 0.3182 | 0.12 | 12500 | 0.2646 | 0.2279 | | 0.3281 | 0.13 | 12600 | 0.2660 | 0.2229 | | 0.3444 | 0.13 | 12700 | 0.2469 | 0.2128 | | 0.3323 | 0.13 | 12800 | 0.2526 | 0.2178 | | 0.3248 | 0.13 | 12900 | 0.2558 | 0.2157 | | 0.3317 | 0.13 | 13000 | 0.2454 | 0.2157 | | 0.3441 | 1.0 | 13100 | 0.2380 | 0.2243 | | 0.3359 | 1.0 | 13200 | 0.2251 | 0.2157 | | 0.3413 | 1.0 | 13300 | 0.2310 | 0.2142 | | 0.3283 | 1.0 | 13400 | 0.2275 | 0.2193 | | 0.3171 | 1.0 | 13500 | 0.2290 | 0.2236 | | 0.3153 | 1.01 | 13600 | 0.2341 | 0.2229 | | 0.3143 | 1.01 | 13700 | 0.2358 | 0.2243 | | 0.3401 | 1.01 | 13800 | 0.2374 | 0.2157 | | 0.2979 | 1.01 | 13900 | 0.2335 | 0.2207 | | 0.3075 | 1.01 | 14000 | 0.2288 | 0.2221 | | 0.308 | 1.01 | 14100 | 0.2354 | 0.2221 | | 0.3272 | 1.01 | 14200 | 0.2339 | 0.2214 | | 0.2748 | 1.01 | 14300 | 0.2411 | 0.2286 | | 0.258 | 1.01 | 14400 | 0.3018 | 0.2121 | | 0.2607 | 1.01 | 14500 | 0.2944 | 0.2142 | | 0.2526 | 1.02 | 14600 | 0.3000 | 0.2178 | | 0.2522 | 1.02 | 14700 | 0.2988 | 0.2185 | | 0.2374 | 1.02 | 14800 | 0.2888 | 0.2150 | | 0.253 | 1.02 | 14900 | 0.2888 | 0.2135 | | 0.2349 | 1.02 | 15000 | 0.3067 | 0.2106 | | 0.2511 | 1.02 | 15100 | 0.2910 | 0.2128 | | 0.2428 | 1.02 | 15200 | 0.2937 | 0.2114 | | 0.2262 | 1.02 | 15300 | 0.3026 | 0.2193 | | 0.2467 | 1.02 | 15400 | 0.2996 | 0.2221 | | 0.2243 | 1.02 | 15500 | 0.3104 | 0.2049 | | 0.2423 | 1.03 | 15600 | 0.2798 | 0.2114 | | 0.2339 | 1.03 | 15700 | 0.2699 | 0.2128 | | 0.2448 | 1.03 | 15800 | 0.3051 | 0.2106 | | 0.2373 | 1.03 | 15900 | 0.3193 | 0.2135 | | 0.249 | 1.03 | 16000 | 0.2992 | 0.2085 | | 0.2473 | 1.03 | 16100 | 0.2982 | 0.2135 | | 0.2427 | 1.03 | 16200 | 0.3118 | 0.2150 | | 0.2439 | 1.03 | 16300 | 0.3238 | 0.2106 | | 0.2317 | 1.03 | 16400 | 0.3075 | 0.2092 | | 0.2257 | 1.03 | 16500 | 0.3110 | 0.2243 | | 0.2418 | 1.04 | 16600 | 0.3005 | 0.2150 | | 0.2264 | 1.04 | 16700 | 0.2978 | 0.2200 | | 0.2389 | 1.04 | 16800 | 0.3078 | 0.2035 | | 0.2457 | 1.04 | 16900 | 0.3227 | 0.2142 | | 0.2479 | 1.04 | 17000 | 0.2922 | 0.2106 | | 0.242 | 1.04 | 17100 | 0.2943 | 0.2099 | | 0.2218 | 1.04 | 17200 | 0.3123 | 0.2099 | | 0.2442 | 1.04 | 17300 | 0.3217 | 0.2157 | | 0.2467 | 1.04 | 17400 | 0.3133 | 0.2078 | | 0.2296 | 1.04 | 17500 | 0.3113 | 0.2128 | | 0.2272 | 1.05 | 17600 | 0.3082 | 0.2085 | | 0.2462 | 1.05 | 17700 | 0.3170 | 0.2121 | | 0.2378 | 1.05 | 17800 | 0.3133 | 0.2150 | | 0.244 | 1.05 | 17900 | 0.3041 | 0.2092 | | 0.232 | 1.05 | 18000 | 0.3113 | 0.2078 | | 0.2511 | 1.05 | 18100 | 0.2830 | 0.2078 | | 0.2487 | 1.05 | 18200 | 0.3015 | 0.2157 | | 0.2302 | 1.05 | 18300 | 0.2813 | 0.2049 | | 0.2256 | 1.05 | 18400 | 0.3110 | 0.1999 | | 0.2273 | 1.05 | 18500 | 0.3183 | 0.2020 | | 0.2256 | 1.06 | 18600 | 0.3083 | 0.2027 | | 0.2242 | 1.06 | 18700 | 0.3042 | 0.1977 | | 0.2436 | 1.06 | 18800 | 0.2861 | 0.1934 | | 0.2345 | 1.06 | 18900 | 0.2876 | 0.1941 | | 0.2482 | 1.06 | 19000 | 0.3003 | 0.2027 | | 0.2236 | 1.06 | 19100 | 0.3085 | 0.1955 | | 0.2199 | 1.06 | 19200 | 0.3150 | 0.2006 | | 0.2305 | 1.06 | 19300 | 0.3172 | 0.2020 | | 0.2395 | 1.06 | 19400 | 0.2879 | 0.2013 | | 0.2301 | 1.06 | 19500 | 0.2818 | 0.2013 | | 0.2496 | 1.07 | 19600 | 0.2883 | 0.2049 | | 0.2332 | 1.07 | 19700 | 0.2962 | 0.2078 | | 0.2193 | 1.07 | 19800 | 0.3092 | 0.2020 | | 0.2268 | 1.07 | 19900 | 0.3079 | 0.2027 | | 0.2262 | 1.07 | 20000 | 0.2986 | 0.2013 | | 0.2391 | 1.07 | 20100 | 0.2974 | 0.1927 | | 0.2374 | 1.07 | 20200 | 0.2993 | 0.1999 | | 0.2319 | 1.07 | 20300 | 0.2974 | 0.2056 | | 0.2273 | 1.07 | 20400 | 0.3122 | 0.2049 | | 0.2303 | 1.07 | 20500 | 0.3386 | 0.2114 | | 0.2362 | 1.08 | 20600 | 0.2870 | 0.2085 | | 0.242 | 1.08 | 20700 | 0.2837 | 0.2135 | | 0.3666 | 1.08 | 20800 | 0.2503 | 0.2171 | | 0.3604 | 1.08 | 20900 | 0.2357 | 0.2085 | | 0.3385 | 1.08 | 21000 | 0.2360 | 0.2085 | | 0.3461 | 1.08 | 21100 | 0.2391 | 0.2070 | | 0.3348 | 1.08 | 21200 | 0.2389 | 0.2106 | | 0.3415 | 1.08 | 21300 | 0.2364 | 0.2128 | | 0.36 | 1.08 | 21400 | 0.2356 | 0.2135 | | 0.3513 | 1.08 | 21500 | 0.2439 | 0.2070 | | 0.3097 | 1.09 | 21600 | 0.2308 | 0.2027 | | 0.3396 | 1.09 | 21700 | 0.2405 | 0.2070 | | 0.3427 | 1.09 | 21800 | 0.2391 | 0.2078 | | 0.3612 | 1.09 | 21900 | 0.2463 | 0.2027 | | 0.3626 | 1.09 | 22000 | 0.2335 | 0.2178 | | 0.3252 | 1.09 | 22100 | 0.2361 | 0.2027 | | 0.314 | 1.09 | 22200 | 0.2319 | 0.2049 | | 0.3394 | 1.09 | 22300 | 0.2342 | 0.2078 | | 0.3313 | 1.09 | 22400 | 0.2425 | 0.2056 | | 0.3414 | 1.09 | 22500 | 0.2311 | 0.2085 | | 0.3307 | 1.1 | 22600 | 0.2347 | 0.1991 | | 0.3436 | 1.1 | 22700 | 0.2515 | 0.2063 | | 0.3221 | 1.1 | 22800 | 0.2415 | 0.2013 | | 0.3272 | 1.1 | 22900 | 0.2275 | 0.2035 | | 0.3193 | 1.1 | 23000 | 0.2321 | 0.2013 | | 0.329 | 1.1 | 23100 | 0.2319 | 0.1991 | | 0.3451 | 1.1 | 23200 | 0.2306 | 0.2070 | | 0.3312 | 1.1 | 23300 | 0.2385 | 0.1984 | | 0.3266 | 1.1 | 23400 | 0.2372 | 0.2157 | | 0.3258 | 1.1 | 23500 | 0.2401 | 0.2128 | | 0.3178 | 1.11 | 23600 | 0.2453 | 0.2042 | | 0.3253 | 1.11 | 23700 | 0.2451 | 0.2171 | | 0.3308 | 1.11 | 23800 | 0.2309 | 0.2027 | | 0.3243 | 1.11 | 23900 | 0.2411 | 0.2085 | | 0.3225 | 1.11 | 24000 | 0.2352 | 0.2049 | | 0.34 | 1.11 | 24100 | 0.2376 | 0.2063 | | 0.3474 | 1.11 | 24200 | 0.2374 | 0.2056 | | 0.3284 | 1.11 | 24300 | 0.2346 | 0.2135 | | 0.3141 | 1.11 | 24400 | 0.2519 | 0.2078 | | 0.3255 | 1.11 | 24500 | 0.2381 | 0.2078 | | 0.3397 | 1.12 | 24600 | 0.2345 | 0.2114 | | 0.3372 | 1.12 | 24700 | 0.2284 | 0.2106 | | 0.3403 | 1.12 | 24800 | 0.2273 | 0.2128 | | 0.3219 | 1.12 | 24900 | 0.2499 | 0.2063 | | 0.3172 | 1.12 | 25000 | 0.2445 | 0.2106 | | 0.3123 | 1.12 | 25100 | 0.2279 | 0.2142 | | 0.3097 | 1.12 | 25200 | 0.2463 | 0.2135 | | 0.3214 | 1.12 | 25300 | 0.2353 | 0.2114 | | 0.3357 | 1.12 | 25400 | 0.2568 | 0.2121 | | 0.3239 | 1.12 | 25500 | 0.2553 | 0.2114 | | 0.3124 | 1.13 | 25600 | 0.2418 | 0.2200 | | 0.3068 | 1.13 | 25700 | 0.2422 | 0.2150 | | 0.3064 | 1.13 | 25800 | 0.2451 | 0.2070 | | 0.2955 | 1.13 | 25900 | 0.2310 | 0.2085 | | 0.3152 | 1.13 | 26000 | 0.2219 | 0.2099 | | 0.2934 | 1.13 | 26100 | 0.2289 | 0.2135 | | 0.3373 | 2.0 | 26200 | 0.2199 | 0.2164 | | 0.3294 | 2.0 | 26300 | 0.2121 | 0.2078 | | 0.3155 | 2.0 | 26400 | 0.2132 | 0.2092 | | 0.3139 | 2.0 | 26500 | 0.2170 | 0.2135 | | 0.3186 | 2.0 | 26600 | 0.2181 | 0.2092 | | 0.2963 | 2.01 | 26700 | 0.2168 | 0.2142 | | 0.3143 | 2.01 | 26800 | 0.2130 | 0.2092 | | 0.3159 | 2.01 | 26900 | 0.2139 | 0.2099 | | 0.2978 | 2.01 | 27000 | 0.2196 | 0.2193 | | 0.2888 | 2.01 | 27100 | 0.2162 | 0.2121 | | 0.2854 | 2.01 | 27200 | 0.2159 | 0.2135 | | 0.2913 | 2.01 | 27300 | 0.2168 | 0.2121 | | 0.2569 | 2.01 | 27400 | 0.2662 | 0.2114 | | 0.2275 | 2.01 | 27500 | 0.2800 | 0.2006 | | 0.2397 | 2.01 | 27600 | 0.2693 | 0.2114 | | 0.2453 | 2.02 | 27700 | 0.2756 | 0.2070 | | 0.233 | 2.02 | 27800 | 0.2884 | 0.2049 | | 0.2226 | 2.02 | 27900 | 0.2765 | 0.2078 | | 0.2278 | 2.02 | 28000 | 0.2944 | 0.2013 | | 0.2241 | 2.02 | 28100 | 0.2897 | 0.2027 | | 0.2388 | 2.02 | 28200 | 0.2881 | 0.2020 | | 0.2301 | 2.02 | 28300 | 0.2844 | 0.1963 | | 0.2164 | 2.02 | 28400 | 0.2978 | 0.2020 | | 0.2286 | 2.02 | 28500 | 0.2919 | 0.1991 | | 0.222 | 2.02 | 28600 | 0.2748 | 0.1970 | | 0.2257 | 2.03 | 28700 | 0.2768 | 0.1984 | | 0.222 | 2.03 | 28800 | 0.2665 | 0.1948 | | 0.2333 | 2.03 | 28900 | 0.2796 | 0.1999 | | 0.2243 | 2.03 | 29000 | 0.2913 | 0.2020 | | 0.2556 | 2.03 | 29100 | 0.2831 | 0.1891 | | 0.2185 | 2.03 | 29200 | 0.2784 | 0.1970 | | 0.2207 | 2.03 | 29300 | 0.2896 | 0.1884 | | 0.2326 | 2.03 | 29400 | 0.2907 | 0.2035 | | 0.211 | 2.03 | 29500 | 0.2868 | 0.1927 | | 0.2156 | 2.03 | 29600 | 0.2917 | 0.1977 | | 0.2311 | 2.04 | 29700 | 0.2749 | 0.1999 | | 0.2131 | 2.04 | 29800 | 0.2994 | 0.1905 | | 0.2251 | 2.04 | 29900 | 0.2868 | 0.1970 | | 0.2252 | 2.04 | 30000 | 0.2845 | 0.1999 | | 0.2284 | 2.04 | 30100 | 0.2898 | 0.2035 | | 0.2256 | 2.04 | 30200 | 0.2919 | 0.2006 | | 0.2139 | 2.04 | 30300 | 0.2977 | 0.1999 | | 0.2326 | 2.04 | 30400 | 0.2881 | 0.2078 | | 0.2247 | 2.04 | 30500 | 0.2769 | 0.2027 | | 0.226 | 2.04 | 30600 | 0.2787 | 0.2035 | | 0.2234 | 2.05 | 30700 | 0.2764 | 0.2035 | | 0.2268 | 2.05 | 30800 | 0.2925 | 0.1977 | | 0.2113 | 2.05 | 30900 | 0.2949 | 0.2035 | | 0.2301 | 2.05 | 31000 | 0.2882 | 0.2020 | | 0.2153 | 2.05 | 31100 | 0.2915 | 0.1984 | | 0.2341 | 2.05 | 31200 | 0.2841 | 0.2013 | | 0.2276 | 2.05 | 31300 | 0.2804 | 0.1934 | | 0.2244 | 2.05 | 31400 | 0.2824 | 0.1999 | | 0.2088 | 2.05 | 31500 | 0.2928 | 0.1999 | | 0.2073 | 2.05 | 31600 | 0.2911 | 0.1977 | | 0.2232 | 2.06 | 31700 | 0.2925 | 0.1991 | | 0.2197 | 2.06 | 31800 | 0.2799 | 0.1999 | | 0.2265 | 2.06 | 31900 | 0.2780 | 0.1927 | | 0.2246 | 2.06 | 32000 | 0.2841 | 0.1955 | | 0.2289 | 2.06 | 32100 | 0.2702 | 0.1941 | | 0.2118 | 2.06 | 32200 | 0.2926 | 0.1999 | | 0.2127 | 2.06 | 32300 | 0.2932 | 0.1977 | | 0.2227 | 2.06 | 32400 | 0.2883 | 0.1977 | | 0.2213 | 2.06 | 32500 | 0.2920 | 0.1999 | | 0.2352 | 2.06 | 32600 | 0.2742 | 0.2063 | | 0.2157 | 2.07 | 32700 | 0.2796 | 0.1955 | | 0.2157 | 2.07 | 32800 | 0.2870 | 0.2063 | | 0.2085 | 2.07 | 32900 | 0.2765 | 0.2049 | | 0.2138 | 2.07 | 33000 | 0.2915 | 0.2078 | | 0.2145 | 2.07 | 33100 | 0.2912 | 0.1927 | | 0.2104 | 2.07 | 33200 | 0.2702 | 0.1898 | | 0.2196 | 2.07 | 33300 | 0.2677 | 0.1891 | | 0.2265 | 2.07 | 33400 | 0.2855 | 0.1884 | | 0.2132 | 2.07 | 33500 | 0.2962 | 0.1970 | | 0.2202 | 2.07 | 33600 | 0.2948 | 0.1934 | | 0.2253 | 2.08 | 33700 | 0.2820 | 0.2013 | | 0.2749 | 2.08 | 33800 | 0.2453 | 0.1991 | | 0.3476 | 2.08 | 33900 | 0.2301 | 0.2035 | | 0.3255 | 2.08 | 34000 | 0.2231 | 0.1948 | | 0.3219 | 2.08 | 34100 | 0.2354 | 0.1948 | | 0.3436 | 2.08 | 34200 | 0.2154 | 0.1999 | | 0.3203 | 2.08 | 34300 | 0.2268 | 0.1948 | | 0.3551 | 2.08 | 34400 | 0.2189 | 0.1970 | | 0.3304 | 2.08 | 34500 | 0.2204 | 0.1934 | | 0.3227 | 2.08 | 34600 | 0.2222 | 0.2020 | | 0.3117 | 2.09 | 34700 | 0.2287 | 0.1927 | | 0.3231 | 2.09 | 34800 | 0.2229 | 0.1884 | | 0.3302 | 2.09 | 34900 | 0.2262 | 0.1977 | | 0.3522 | 2.09 | 35000 | 0.2313 | 0.2013 | | 0.3218 | 2.09 | 35100 | 0.2218 | 0.1934 | | 0.309 | 2.09 | 35200 | 0.2227 | 0.1905 | | 0.3114 | 2.09 | 35300 | 0.2181 | 0.1891 | | 0.3215 | 2.09 | 35400 | 0.2334 | 0.1963 | | 0.3129 | 2.09 | 35500 | 0.2307 | 0.2027 | | 0.3285 | 2.09 | 35600 | 0.2311 | 0.2006 | | 0.3122 | 2.1 | 35700 | 0.2181 | 0.2070 | | 0.3122 | 2.1 | 35800 | 0.2253 | 0.1934 | | 0.3259 | 2.1 | 35900 | 0.2295 | 0.1934 | | 0.3175 | 2.1 | 36000 | 0.2362 | 0.1977 | | 0.3005 | 2.1 | 36100 | 0.2203 | 0.2013 | | 0.3379 | 2.1 | 36200 | 0.2278 | 0.1934 | | 0.3254 | 2.1 | 36300 | 0.2236 | 0.1891 | | 0.2961 | 2.1 | 36400 | 0.2200 | 0.1927 | | 0.3145 | 2.1 | 36500 | 0.2422 | 0.1984 | | 0.3312 | 2.1 | 36600 | 0.2243 | 0.1991 | | 0.297 | 2.11 | 36700 | 0.2180 | 0.2006 | | 0.2973 | 2.11 | 36800 | 0.2261 | 0.1963 | | 0.3078 | 2.11 | 36900 | 0.2255 | 0.1970 | | 0.3081 | 2.11 | 37000 | 0.2349 | 0.2020 | | 0.3069 | 2.11 | 37100 | 0.2189 | 0.1941 | | 0.3339 | 2.11 | 37200 | 0.2242 | 0.1919 | | 0.319 | 2.11 | 37300 | 0.2286 | 0.1927 | | 0.3219 | 2.11 | 37400 | 0.2284 | 0.1999 | | 0.2991 | 2.11 | 37500 | 0.2315 | 0.1948 | | 0.3165 | 2.11 | 37600 | 0.2203 | 0.2006 | | 0.3157 | 2.12 | 37700 | 0.2298 | 0.1970 | | 0.3226 | 2.12 | 37800 | 0.2335 | 0.1963 | | 0.3172 | 2.12 | 37900 | 0.2177 | 0.1963 | | 0.2901 | 2.12 | 38000 | 0.2308 | 0.1970 | | 0.3084 | 2.12 | 38100 | 0.2435 | 0.2020 | | 0.2965 | 2.12 | 38200 | 0.2261 | 0.1970 | | 0.2859 | 2.12 | 38300 | 0.2279 | 0.1963 | | 0.3067 | 2.12 | 38400 | 0.2264 | 0.1905 | | 0.3078 | 2.12 | 38500 | 0.2356 | 0.1999 | | 0.3018 | 2.12 | 38600 | 0.2523 | 0.1884 | | 0.3006 | 2.13 | 38700 | 0.2379 | 0.1991 | | 0.2953 | 2.13 | 38800 | 0.2335 | 0.2035 | | 0.3118 | 2.13 | 38900 | 0.2305 | 0.2085 | | 0.2932 | 2.13 | 39000 | 0.2283 | 0.1984 | | 0.2949 | 2.13 | 39100 | 0.2304 | 0.1941 | | 0.2936 | 2.13 | 39200 | 0.2343 | 0.1970 | | 0.3143 | 3.0 | 39300 | 0.2083 | 0.1955 | | 0.3219 | 3.0 | 39400 | 0.2092 | 0.1963 | | 0.3121 | 3.0 | 39500 | 0.2110 | 0.1934 | | 0.3077 | 3.0 | 39600 | 0.2065 | 0.2027 | | 0.2991 | 3.0 | 39700 | 0.2082 | 0.2070 | | 0.2991 | 3.01 | 39800 | 0.2071 | 0.2013 | | 0.3002 | 3.01 | 39900 | 0.2076 | 0.1999 | | 0.2958 | 3.01 | 40000 | 0.2112 | 0.1955 | | 0.2903 | 3.01 | 40100 | 0.2092 | 0.1948 | | 0.2836 | 3.01 | 40200 | 0.2115 | 0.1948 | | 0.2909 | 3.01 | 40300 | 0.2089 | 0.1948 | | 0.2819 | 3.01 | 40400 | 0.2111 | 0.1919 | | 0.2443 | 3.01 | 40500 | 0.2712 | 0.1941 | | 0.2375 | 3.01 | 40600 | 0.2530 | 0.1919 | | 0.2368 | 3.01 | 40700 | 0.2631 | 0.1955 | | 0.225 | 3.02 | 40800 | 0.2684 | 0.1884 | | 0.2296 | 3.02 | 40900 | 0.2657 | 0.1955 | | 0.2193 | 3.02 | 41000 | 0.2657 | 0.1898 | | 0.2118 | 3.02 | 41100 | 0.2737 | 0.1891 | | 0.2155 | 3.02 | 41200 | 0.2821 | 0.1948 | | 0.2298 | 3.02 | 41300 | 0.2765 | 0.1891 | | 0.2067 | 3.02 | 41400 | 0.2724 | 0.1898 | | 0.2065 | 3.02 | 41500 | 0.2820 | 0.1848 | | 0.218 | 3.02 | 41600 | 0.2782 | 0.1891 | | 0.212 | 3.02 | 41700 | 0.2724 | 0.1941 | | 0.2109 | 3.03 | 41800 | 0.2715 | 0.1891 | | 0.2094 | 3.03 | 41900 | 0.2687 | 0.1876 | | 0.2256 | 3.03 | 42000 | 0.2843 | 0.1919 | | 0.2156 | 3.03 | 42100 | 0.2742 | 0.1905 | | 0.2397 | 3.03 | 42200 | 0.2744 | 0.1941 | | 0.2097 | 3.03 | 42300 | 0.2690 | 0.1869 | | 0.228 | 3.03 | 42400 | 0.2614 | 0.2042 | | 0.2105 | 3.03 | 42500 | 0.2782 | 0.1833 | | 0.2088 | 3.03 | 42600 | 0.2973 | 0.1912 | | 0.2165 | 3.03 | 42700 | 0.2891 | 0.1898 | | 0.2108 | 3.04 | 42800 | 0.2601 | 0.1905 | | 0.2059 | 3.04 | 42900 | 0.2823 | 0.1919 | | 0.218 | 3.04 | 43000 | 0.2801 | 0.1898 | | 0.2198 | 3.04 | 43100 | 0.2717 | 0.1848 | | 0.2244 | 3.04 | 43200 | 0.2548 | 0.1955 | | 0.2158 | 3.04 | 43300 | 0.2697 | 0.1963 | | 0.2093 | 3.04 | 43400 | 0.2917 | 0.1970 | | 0.2283 | 3.04 | 43500 | 0.2666 | 0.1912 | | 0.2071 | 3.04 | 43600 | 0.2588 | 0.1891 | | 0.2122 | 3.04 | 43700 | 0.2674 | 0.1876 | | 0.2181 | 3.05 | 43800 | 0.2882 | 0.1941 | | 0.218 | 3.05 | 43900 | 0.2624 | 0.1898 | | 0.2075 | 3.05 | 44000 | 0.2743 | 0.1819 | | 0.2208 | 3.05 | 44100 | 0.2809 | 0.1912 | | 0.2221 | 3.05 | 44200 | 0.2728 | 0.1919 | | 0.222 | 3.05 | 44300 | 0.2790 | 0.1855 | | 0.2254 | 3.05 | 44400 | 0.2683 | 0.1884 | | 0.2153 | 3.05 | 44500 | 0.2624 | 0.2013 | | 0.2038 | 3.05 | 44600 | 0.2732 | 0.1905 | | 0.1955 | 3.05 | 44700 | 0.2570 | 0.1840 | | 0.2203 | 3.06 | 44800 | 0.2851 | 0.1812 | | 0.2032 | 3.06 | 44900 | 0.2646 | 0.1833 | | 0.2201 | 3.06 | 45000 | 0.2763 | 0.1848 | | 0.2129 | 3.06 | 45100 | 0.2844 | 0.1891 | | 0.2276 | 3.06 | 45200 | 0.2646 | 0.1840 | | 0.205 | 3.06 | 45300 | 0.2802 | 0.1862 | | 0.2164 | 3.06 | 45400 | 0.2687 | 0.1797 | | 0.2226 | 3.06 | 45500 | 0.2732 | 0.1804 | | 0.2061 | 3.06 | 45600 | 0.2829 | 0.1855 | | 0.2184 | 3.06 | 45700 | 0.2676 | 0.1919 | | 0.2151 | 3.07 | 45800 | 0.2881 | 0.1855 | | 0.2118 | 3.07 | 45900 | 0.2780 | 0.1855 | | 0.2007 | 3.07 | 46000 | 0.2674 | 0.1855 | | 0.206 | 3.07 | 46100 | 0.2828 | 0.1884 | | 0.2171 | 3.07 | 46200 | 0.2843 | 0.1783 | | 0.2136 | 3.07 | 46300 | 0.2782 | 0.1855 | | 0.2123 | 3.07 | 46400 | 0.2730 | 0.1876 | | 0.2197 | 3.07 | 46500 | 0.2881 | 0.1819 | | 0.1985 | 3.07 | 46600 | 0.2831 | 0.1848 | | 0.2174 | 3.07 | 46700 | 0.2676 | 0.1769 | | 0.2144 | 3.08 | 46800 | 0.2916 | 0.1840 | | 0.2974 | 3.08 | 46900 | 0.2193 | 0.1869 | | 0.3292 | 3.08 | 47000 | 0.2193 | 0.1898 | | 0.3086 | 3.08 | 47100 | 0.2194 | 0.1840 | | 0.3122 | 3.08 | 47200 | 0.2285 | 0.1941 | | 0.3243 | 3.08 | 47300 | 0.2159 | 0.1912 | | 0.3107 | 3.08 | 47400 | 0.2226 | 0.1862 | | 0.3441 | 3.08 | 47500 | 0.2195 | 0.1833 | | 0.3099 | 3.08 | 47600 | 0.2210 | 0.1927 | | 0.2827 | 3.08 | 47700 | 0.2297 | 0.1891 | | 0.3002 | 3.09 | 47800 | 0.2242 | 0.1898 | | 0.3076 | 3.09 | 47900 | 0.2242 | 0.1855 | | 0.3199 | 3.09 | 48000 | 0.2179 | 0.1898 | | 0.3239 | 3.09 | 48100 | 0.2228 | 0.1840 | | 0.3069 | 3.09 | 48200 | 0.2191 | 0.1855 | | 0.3061 | 3.09 | 48300 | 0.2075 | 0.1898 | | 0.3129 | 3.09 | 48400 | 0.2223 | 0.1891 | | 0.3134 | 3.09 | 48500 | 0.2247 | 0.1912 | | 0.3198 | 3.09 | 48600 | 0.2137 | 0.1876 | | 0.3209 | 3.09 | 48700 | 0.2263 | 0.1898 | | 0.3193 | 3.1 | 48800 | 0.2256 | 0.1869 | | 0.3247 | 3.1 | 48900 | 0.2220 | 0.1898 | | 0.3112 | 3.1 | 49000 | 0.2166 | 0.1891 | | 0.2954 | 3.1 | 49100 | 0.2234 | 0.1862 | | 0.2933 | 3.1 | 49200 | 0.2172 | 0.1848 | | 0.3214 | 3.1 | 49300 | 0.2198 | 0.1941 | | 0.3241 | 3.1 | 49400 | 0.2142 | 0.1869 | | 0.3025 | 3.1 | 49500 | 0.2218 | 0.1912 | | 0.3069 | 3.1 | 49600 | 0.2306 | 0.1819 | | 0.3089 | 3.1 | 49700 | 0.2203 | 0.1862 | | 0.299 | 3.11 | 49800 | 0.2155 | 0.1884 | | 0.3079 | 3.11 | 49900 | 0.2225 | 0.1862 | | 0.3123 | 3.11 | 50000 | 0.2225 | 0.1891 | | 0.2964 | 3.11 | 50100 | 0.2199 | 0.1869 | | 0.3143 | 3.11 | 50200 | 0.2181 | 0.1991 | | 0.3266 | 3.11 | 50300 | 0.2178 | 0.1912 | | 0.3114 | 3.11 | 50400 | 0.2132 | 0.1862 | | 0.2994 | 3.11 | 50500 | 0.2152 | 0.1927 | | 0.2932 | 3.11 | 50600 | 0.2186 | 0.1891 | | 0.3215 | 3.11 | 50700 | 0.2150 | 0.1819 | | 0.3103 | 3.12 | 50800 | 0.2153 | 0.1905 | | 0.3129 | 3.12 | 50900 | 0.2223 | 0.1905 | | 0.3167 | 3.12 | 51000 | 0.2185 | 0.1884 | | 0.2932 | 3.12 | 51100 | 0.2316 | 0.1876 | | 0.2968 | 3.12 | 51200 | 0.2314 | 0.1919 | | 0.2884 | 3.12 | 51300 | 0.2220 | 0.1783 | | 0.2943 | 3.12 | 51400 | 0.2239 | 0.1912 | | 0.2994 | 3.12 | 51500 | 0.2139 | 0.1833 | | 0.3172 | 3.12 | 51600 | 0.2319 | 0.1919 | | 0.2828 | 3.12 | 51700 | 0.2315 | 0.1991 | | 0.3104 | 3.13 | 51800 | 0.2253 | 0.1948 | | 0.285 | 3.13 | 51900 | 0.2143 | 0.1963 | | 0.2916 | 3.13 | 52000 | 0.2237 | 0.1970 | | 0.2787 | 3.13 | 52100 | 0.2177 | 0.1984 | | 0.2909 | 3.13 | 52200 | 0.2290 | 0.1912 | | 0.2967 | 4.0 | 52300 | 0.2107 | 0.1955 | | 0.3057 | 4.0 | 52400 | 0.2052 | 0.1869 | | 0.3248 | 4.0 | 52500 | 0.1982 | 0.1812 | | 0.3072 | 4.0 | 52600 | 0.1969 | 0.1754 | | 0.2967 | 4.0 | 52700 | 0.1981 | 0.1804 | | 0.2984 | 4.01 | 52800 | 0.2031 | 0.1826 | | 0.2878 | 4.01 | 52900 | 0.2015 | 0.1927 | | 0.308 | 4.01 | 53000 | 0.2009 | 0.1919 | | 0.2843 | 4.01 | 53100 | 0.2020 | 0.1912 | | 0.2678 | 4.01 | 53200 | 0.2017 | 0.1819 | | 0.2779 | 4.01 | 53300 | 0.2041 | 0.1812 | | 0.2886 | 4.01 | 53400 | 0.1994 | 0.1905 | | 0.2419 | 4.01 | 53500 | 0.2083 | 0.1833 | | 0.2317 | 4.01 | 53600 | 0.2683 | 0.1848 | | 0.223 | 4.01 | 53700 | 0.2444 | 0.1862 | | 0.2385 | 4.02 | 53800 | 0.2605 | 0.1819 | | 0.2208 | 4.02 | 53900 | 0.2630 | 0.1912 | | 0.2116 | 4.02 | 54000 | 0.2589 | 0.1833 | | 0.2188 | 4.02 | 54100 | 0.2489 | 0.1797 | | 0.2001 | 4.02 | 54200 | 0.2675 | 0.1812 | | 0.212 | 4.02 | 54300 | 0.2607 | 0.1761 | | 0.2163 | 4.02 | 54400 | 0.2636 | 0.1783 | | 0.1991 | 4.02 | 54500 | 0.2659 | 0.1804 | | 0.2156 | 4.02 | 54600 | 0.2517 | 0.1769 | | 0.1987 | 4.02 | 54700 | 0.2736 | 0.1833 | | 0.2122 | 4.03 | 54800 | 0.2412 | 0.1754 | | 0.2072 | 4.03 | 54900 | 0.2512 | 0.1869 | | 0.2043 | 4.03 | 55000 | 0.2564 | 0.1819 | | 0.2139 | 4.03 | 55100 | 0.2756 | 0.1840 | | 0.2211 | 4.03 | 55200 | 0.2683 | 0.1826 | | 0.2114 | 4.03 | 55300 | 0.2725 | 0.1769 | | 0.2002 | 4.03 | 55400 | 0.2584 | 0.1797 | | 0.2106 | 4.03 | 55500 | 0.2793 | 0.1790 | | 0.2075 | 4.03 | 55600 | 0.2626 | 0.1826 | | 0.2057 | 4.03 | 55700 | 0.2635 | 0.1783 | | 0.2126 | 4.04 | 55800 | 0.2661 | 0.1776 | | 0.2072 | 4.04 | 55900 | 0.2584 | 0.1776 | | 0.2039 | 4.04 | 56000 | 0.2740 | 0.1826 | | 0.2138 | 4.04 | 56100 | 0.2700 | 0.1797 | | 0.2082 | 4.04 | 56200 | 0.2527 | 0.1876 | | 0.213 | 4.04 | 56300 | 0.2631 | 0.1833 | | 0.191 | 4.04 | 56400 | 0.2673 | 0.1812 | | 0.2026 | 4.04 | 56500 | 0.2681 | 0.1855 | | 0.221 | 4.04 | 56600 | 0.2660 | 0.1797 | | 0.2026 | 4.04 | 56700 | 0.2719 | 0.1819 | | 0.1954 | 4.05 | 56800 | 0.2785 | 0.1747 | | 0.2111 | 4.05 | 56900 | 0.2755 | 0.1812 | | 0.2077 | 4.05 | 57000 | 0.2726 | 0.1848 | | 0.2025 | 4.05 | 57100 | 0.2690 | 0.1884 | | 0.2167 | 4.05 | 57200 | 0.2719 | 0.1869 | | 0.2062 | 4.05 | 57300 | 0.2660 | 0.1819 | | 0.2245 | 4.05 | 57400 | 0.2756 | 0.1776 | | 0.2185 | 4.05 | 57500 | 0.2668 | 0.1776 | | 0.1968 | 4.05 | 57600 | 0.2810 | 0.1776 | | 0.2016 | 4.05 | 57700 | 0.2894 | 0.1776 | | 0.1921 | 4.06 | 57800 | 0.2772 | 0.1797 | | 0.2078 | 4.06 | 57900 | 0.2874 | 0.1862 | | 0.209 | 4.06 | 58000 | 0.2643 | 0.1769 | | 0.2095 | 4.06 | 58100 | 0.2635 | 0.1819 | | 0.2098 | 4.06 | 58200 | 0.2710 | 0.1797 | | 0.2088 | 4.06 | 58300 | 0.2700 | 0.1747 | | 0.202 | 4.06 | 58400 | 0.2748 | 0.1783 | | 0.2113 | 4.06 | 58500 | 0.2794 | 0.1819 | | 0.2108 | 4.06 | 58600 | 0.2658 | 0.1804 | | 0.2001 | 4.06 | 58700 | 0.2764 | 0.1797 | | 0.2171 | 4.07 | 58800 | 0.2689 | 0.1797 | | 0.2024 | 4.07 | 58900 | 0.2509 | 0.1761 | | 0.1994 | 4.07 | 59000 | 0.2769 | 0.1797 | | 0.1923 | 4.07 | 59100 | 0.2518 | 0.1776 | | 0.1998 | 4.07 | 59200 | 0.2672 | 0.1769 | | 0.2075 | 4.07 | 59300 | 0.2704 | 0.1840 | | 0.2056 | 4.07 | 59400 | 0.2723 | 0.1826 | | 0.2107 | 4.07 | 59500 | 0.2671 | 0.1776 | | 0.213 | 4.07 | 59600 | 0.2850 | 0.1797 | | 0.205 | 4.07 | 59700 | 0.2790 | 0.1790 | | 0.2042 | 4.08 | 59800 | 0.2841 | 0.1826 | | 0.2096 | 4.08 | 59900 | 0.2776 | 0.1783 | | 0.3228 | 4.08 | 60000 | 0.2220 | 0.1812 | | 0.3277 | 4.08 | 60100 | 0.2229 | 0.1869 | | 0.311 | 4.08 | 60200 | 0.2323 | 0.1862 | | 0.2944 | 4.08 | 60300 | 0.2147 | 0.1754 | | 0.32 | 4.08 | 60400 | 0.2103 | 0.1783 | | 0.2769 | 4.08 | 60500 | 0.2209 | 0.1797 | | 0.3392 | 4.08 | 60600 | 0.2145 | 0.1783 | | 0.3189 | 4.08 | 60700 | 0.2079 | 0.1840 | | 0.2825 | 4.09 | 60800 | 0.2262 | 0.1869 | | 0.3007 | 4.09 | 60900 | 0.2121 | 0.1855 | | 0.2973 | 4.09 | 61000 | 0.2151 | 0.1819 | | 0.3367 | 4.09 | 61100 | 0.2121 | 0.1869 | | 0.3168 | 4.09 | 61200 | 0.2191 | 0.1747 | | 0.2964 | 4.09 | 61300 | 0.2148 | 0.1804 | | 0.2936 | 4.09 | 61400 | 0.2111 | 0.1783 | | 0.3022 | 4.09 | 61500 | 0.2175 | 0.1812 | | 0.2972 | 4.09 | 61600 | 0.2218 | 0.1833 | | 0.3069 | 4.09 | 61700 | 0.2135 | 0.1826 | | 0.3027 | 4.1 | 61800 | 0.2226 | 0.1812 | | 0.2917 | 4.1 | 61900 | 0.2166 | 0.1812 | | 0.311 | 4.1 | 62000 | 0.2164 | 0.1761 | | 0.3 | 4.1 | 62100 | 0.2227 | 0.1797 | | 0.2809 | 4.1 | 62200 | 0.2151 | 0.1747 | | 0.3062 | 4.1 | 62300 | 0.2139 | 0.1704 | | 0.3063 | 4.1 | 62400 | 0.2184 | 0.1797 | | 0.3006 | 4.1 | 62500 | 0.2087 | 0.1776 | | 0.2898 | 4.1 | 62600 | 0.2180 | 0.1790 | | 0.2937 | 4.1 | 62700 | 0.2124 | 0.1804 | | 0.2906 | 4.11 | 62800 | 0.2219 | 0.1804 | | 0.2842 | 4.11 | 62900 | 0.2163 | 0.1761 | | 0.2911 | 4.11 | 63000 | 0.2210 | 0.1754 | | 0.2983 | 4.11 | 63100 | 0.2236 | 0.1804 | | 0.2948 | 4.11 | 63200 | 0.2132 | 0.1797 | | 0.3152 | 4.11 | 63300 | 0.2132 | 0.1733 | | 0.3081 | 4.11 | 63400 | 0.2119 | 0.1754 | | 0.3145 | 4.11 | 63500 | 0.2123 | 0.1876 | | 0.2867 | 4.11 | 63600 | 0.2149 | 0.1826 | | 0.2827 | 4.11 | 63700 | 0.2097 | 0.1718 | | 0.3117 | 4.12 | 63800 | 0.2143 | 0.1769 | | 0.2909 | 4.12 | 63900 | 0.2184 | 0.1776 | | 0.2971 | 4.12 | 64000 | 0.2187 | 0.1754 | | 0.2895 | 4.12 | 64100 | 0.2139 | 0.1704 | | 0.2885 | 4.12 | 64200 | 0.2291 | 0.1761 | | 0.2848 | 4.12 | 64300 | 0.2132 | 0.1826 | | 0.2951 | 4.12 | 64400 | 0.2136 | 0.1869 | | 0.2839 | 4.12 | 64500 | 0.2149 | 0.1804 | | 0.2983 | 4.12 | 64600 | 0.2146 | 0.1826 | | 0.3029 | 4.12 | 64700 | 0.2327 | 0.1797 | | 0.2775 | 4.13 | 64800 | 0.2222 | 0.1797 | | 0.2813 | 4.13 | 64900 | 0.2234 | 0.1819 | | 0.2822 | 4.13 | 65000 | 0.2126 | 0.1891 | | 0.2757 | 4.13 | 65100 | 0.2183 | 0.1919 | | 0.2792 | 4.13 | 65200 | 0.2147 | 0.1855 | | 0.2909 | 4.13 | 65300 | 0.2157 | 0.1869 | | 0.2946 | 5.0 | 65400 | 0.1955 | 0.1826 | | 0.3022 | 5.0 | 65500 | 0.1938 | 0.1848 | | 0.3086 | 5.0 | 65600 | 0.1910 | 0.1790 | | 0.2887 | 5.0 | 65700 | 0.1915 | 0.1776 | | 0.2941 | 5.0 | 65800 | 0.1924 | 0.1747 | | 0.2906 | 5.01 | 65900 | 0.1933 | 0.1833 | | 0.2876 | 5.01 | 66000 | 0.1967 | 0.1725 | | 0.2992 | 5.01 | 66100 | 0.1926 | 0.1740 | | 0.2769 | 5.01 | 66200 | 0.1940 | 0.1797 | | 0.2703 | 5.01 | 66300 | 0.1980 | 0.1711 | | 0.2777 | 5.01 | 66400 | 0.1996 | 0.1704 | | 0.2908 | 5.01 | 66500 | 0.1954 | 0.1754 | | 0.2467 | 5.01 | 66600 | 0.1982 | 0.1869 | | 0.2192 | 5.01 | 66700 | 0.2626 | 0.1776 | | 0.2227 | 5.01 | 66800 | 0.2472 | 0.1725 | | 0.2192 | 5.02 | 66900 | 0.2449 | 0.1776 | | 0.206 | 5.02 | 67000 | 0.2669 | 0.1769 | | 0.1988 | 5.02 | 67100 | 0.2567 | 0.1747 | | 0.2125 | 5.02 | 67200 | 0.2577 | 0.1790 | | 0.1962 | 5.02 | 67300 | 0.2639 | 0.1747 | | 0.2101 | 5.02 | 67400 | 0.2570 | 0.1697 | | 0.208 | 5.02 | 67500 | 0.2584 | 0.1776 | | 0.1963 | 5.02 | 67600 | 0.2519 | 0.1740 | | 0.2067 | 5.02 | 67700 | 0.2607 | 0.1711 | | 0.1925 | 5.02 | 67800 | 0.2645 | 0.1733 | | 0.2107 | 5.03 | 67900 | 0.2379 | 0.1797 | | 0.1995 | 5.03 | 68000 | 0.2425 | 0.1790 | | 0.2023 | 5.03 | 68100 | 0.2626 | 0.1769 | | 0.2037 | 5.03 | 68200 | 0.2751 | 0.1754 | | 0.2226 | 5.03 | 68300 | 0.2499 | 0.1747 | | 0.2103 | 5.03 | 68400 | 0.2634 | 0.1718 | | 0.2037 | 5.03 | 68500 | 0.2595 | 0.1804 | | 0.2104 | 5.03 | 68600 | 0.2699 | 0.1697 | | 0.1998 | 5.03 | 68700 | 0.2596 | 0.1819 | | 0.2026 | 5.03 | 68800 | 0.2644 | 0.1740 | | 0.2032 | 5.04 | 68900 | 0.2718 | 0.1718 | | 0.1919 | 5.04 | 69000 | 0.2606 | 0.1797 | | 0.2049 | 5.04 | 69100 | 0.2719 | 0.1733 | | 0.2086 | 5.04 | 69200 | 0.2700 | 0.1769 | | 0.2118 | 5.04 | 69300 | 0.2556 | 0.1747 | | 0.2078 | 5.04 | 69400 | 0.2529 | 0.1733 | | 0.1882 | 5.04 | 69500 | 0.2753 | 0.1804 | | 0.2077 | 5.04 | 69600 | 0.2801 | 0.1769 | | 0.2073 | 5.04 | 69700 | 0.2695 | 0.1769 | | 0.1983 | 5.04 | 69800 | 0.2611 | 0.1747 | | 0.2117 | 5.05 | 69900 | 0.2581 | 0.1718 | | 0.1982 | 5.05 | 70000 | 0.2714 | 0.1697 | | 0.201 | 5.05 | 70100 | 0.2596 | 0.1689 | | 0.2084 | 5.05 | 70200 | 0.2617 | 0.1653 | | 0.2003 | 5.05 | 70300 | 0.2681 | 0.1711 | | 0.2173 | 5.05 | 70400 | 0.2590 | 0.1733 | | 0.2118 | 5.05 | 70500 | 0.2595 | 0.1689 | | 0.197 | 5.05 | 70600 | 0.2549 | 0.1754 | | 0.1956 | 5.05 | 70700 | 0.2685 | 0.1718 | | 0.1923 | 5.05 | 70800 | 0.2755 | 0.1769 | | 0.1949 | 5.06 | 70900 | 0.2722 | 0.1776 | | 0.2007 | 5.06 | 71000 | 0.2611 | 0.1769 | | 0.2154 | 5.06 | 71100 | 0.2604 | 0.1740 | | 0.1999 | 5.06 | 71200 | 0.2556 | 0.1747 | | 0.2167 | 5.06 | 71300 | 0.2622 | 0.1797 | | 0.1968 | 5.06 | 71400 | 0.2670 | 0.1718 | | 0.2009 | 5.06 | 71500 | 0.2727 | 0.1747 | | 0.2017 | 5.06 | 71600 | 0.2769 | 0.1826 | | 0.2105 | 5.06 | 71700 | 0.2628 | 0.1776 | | 0.2071 | 5.06 | 71800 | 0.2552 | 0.1848 | | 0.1984 | 5.07 | 71900 | 0.2592 | 0.1704 | | 0.1967 | 5.07 | 72000 | 0.2612 | 0.1733 | | 0.19 | 5.07 | 72100 | 0.2701 | 0.1783 | | 0.2034 | 5.07 | 72200 | 0.2723 | 0.1740 | | 0.1946 | 5.07 | 72300 | 0.2743 | 0.1740 | | 0.2078 | 5.07 | 72400 | 0.2653 | 0.1682 | | 0.2034 | 5.07 | 72500 | 0.2751 | 0.1761 | | 0.2018 | 5.07 | 72600 | 0.2692 | 0.1740 | | 0.1916 | 5.07 | 72700 | 0.2768 | 0.1797 | | 0.2042 | 5.07 | 72800 | 0.2704 | 0.1754 | | 0.2037 | 5.08 | 72900 | 0.2735 | 0.1711 | | 0.2286 | 5.08 | 73000 | 0.2196 | 0.1797 | | 0.3236 | 5.08 | 73100 | 0.2112 | 0.1855 | | 0.2937 | 5.08 | 73200 | 0.2094 | 0.1819 | | 0.2927 | 5.08 | 73300 | 0.2214 | 0.1884 | | 0.2958 | 5.08 | 73400 | 0.2187 | 0.1812 | | 0.303 | 5.08 | 73500 | 0.2153 | 0.1776 | | 0.3022 | 5.08 | 73600 | 0.2164 | 0.1812 | | 0.3054 | 5.08 | 73700 | 0.2028 | 0.1790 | | 0.294 | 5.08 | 73800 | 0.2164 | 0.1697 | | 0.2916 | 5.09 | 73900 | 0.2229 | 0.1747 | | 0.2981 | 5.09 | 74000 | 0.2102 | 0.1776 | | 0.2925 | 5.09 | 74100 | 0.2197 | 0.1790 | | 0.3208 | 5.09 | 74200 | 0.2216 | 0.1783 | | 0.2969 | 5.09 | 74300 | 0.2122 | 0.1790 | | 0.2895 | 5.09 | 74400 | 0.2166 | 0.1804 | | 0.2759 | 5.09 | 74500 | 0.2171 | 0.1769 | | 0.2912 | 5.09 | 74600 | 0.2169 | 0.1689 | | 0.2918 | 5.09 | 74700 | 0.2167 | 0.1840 | | 0.3058 | 5.09 | 74800 | 0.2184 | 0.1740 | | 0.2914 | 5.1 | 74900 | 0.2070 | 0.1747 | | 0.2984 | 5.1 | 75000 | 0.2182 | 0.1740 | | 0.278 | 5.1 | 75100 | 0.2200 | 0.1740 | | 0.2825 | 5.1 | 75200 | 0.2099 | 0.1761 | | 0.2946 | 5.1 | 75300 | 0.2126 | 0.1733 | | 0.2885 | 5.1 | 75400 | 0.2150 | 0.1725 | | 0.2994 | 5.1 | 75500 | 0.2055 | 0.1790 | | 0.2783 | 5.1 | 75600 | 0.2179 | 0.1747 | | 0.2889 | 5.1 | 75700 | 0.2121 | 0.1761 | | 0.2945 | 5.1 | 75800 | 0.2129 | 0.1804 | | 0.2737 | 5.11 | 75900 | 0.2107 | 0.1718 | | 0.286 | 5.11 | 76000 | 0.2124 | 0.1754 | | 0.288 | 5.11 | 76100 | 0.2105 | 0.1740 | | 0.2714 | 5.11 | 76200 | 0.2196 | 0.1740 | | 0.3 | 5.11 | 76300 | 0.2190 | 0.1769 | | 0.3108 | 5.11 | 76400 | 0.2118 | 0.1783 | | 0.3053 | 5.11 | 76500 | 0.2148 | 0.1754 | | 0.3021 | 5.11 | 76600 | 0.2137 | 0.1776 | | 0.2831 | 5.11 | 76700 | 0.2090 | 0.1769 | | 0.2705 | 5.11 | 76800 | 0.2126 | 0.1733 | | 0.3132 | 5.12 | 76900 | 0.2083 | 0.1740 | | 0.2816 | 5.12 | 77000 | 0.2159 | 0.1769 | | 0.2901 | 5.12 | 77100 | 0.2175 | 0.1769 | | 0.2767 | 5.12 | 77200 | 0.2199 | 0.1704 | | 0.2875 | 5.12 | 77300 | 0.2172 | 0.1790 | | 0.279 | 5.12 | 77400 | 0.2186 | 0.1769 | | 0.2784 | 5.12 | 77500 | 0.2276 | 0.1761 | | 0.2965 | 5.12 | 77600 | 0.2161 | 0.1783 | | 0.2895 | 5.12 | 77700 | 0.2276 | 0.1783 | | 0.2753 | 5.12 | 77800 | 0.2280 | 0.1718 | | 0.2775 | 5.13 | 77900 | 0.2241 | 0.1761 | | 0.2644 | 5.13 | 78000 | 0.2263 | 0.1790 | | 0.2909 | 5.13 | 78100 | 0.2221 | 0.1812 | | 0.2622 | 5.13 | 78200 | 0.2178 | 0.1797 | | 0.275 | 5.13 | 78300 | 0.2135 | 0.1783 | | 0.2706 | 5.13 | 78400 | 0.2115 | 0.1783 | | 0.2967 | 6.0 | 78500 | 0.1939 | 0.1761 | | 0.3006 | 6.0 | 78600 | 0.1912 | 0.1761 | | 0.2895 | 6.0 | 78700 | 0.1899 | 0.1718 | | 0.2918 | 6.0 | 78800 | 0.1874 | 0.1804 | | 0.2946 | 6.0 | 78900 | 0.1908 | 0.1776 | | 0.2774 | 6.01 | 79000 | 0.1907 | 0.1761 | | 0.2835 | 6.01 | 79100 | 0.1890 | 0.1718 | | 0.2867 | 6.01 | 79200 | 0.1898 | 0.1747 | | 0.2778 | 6.01 | 79300 | 0.1911 | 0.1769 | | 0.2654 | 6.01 | 79400 | 0.1906 | 0.1761 | | 0.2769 | 6.01 | 79500 | 0.1902 | 0.1761 | | 0.2697 | 6.01 | 79600 | 0.1908 | 0.1747 | | 0.237 | 6.01 | 79700 | 0.2295 | 0.1740 | | 0.2045 | 6.01 | 79800 | 0.2397 | 0.1769 | | 0.2071 | 6.01 | 79900 | 0.2405 | 0.1697 | | 0.2105 | 6.02 | 80000 | 0.2430 | 0.1754 | | 0.1955 | 6.02 | 80100 | 0.2478 | 0.1769 | | 0.196 | 6.02 | 80200 | 0.2424 | 0.1776 | | 0.2045 | 6.02 | 80300 | 0.2508 | 0.1697 | | 0.1948 | 6.02 | 80400 | 0.2571 | 0.1711 | | 0.2096 | 6.02 | 80500 | 0.2477 | 0.1689 | | 0.1928 | 6.02 | 80600 | 0.2503 | 0.1675 | | 0.1888 | 6.02 | 80700 | 0.2540 | 0.1682 | | 0.2006 | 6.02 | 80800 | 0.2587 | 0.1697 | | 0.2008 | 6.02 | 80900 | 0.2546 | 0.1704 | | 0.2018 | 6.03 | 81000 | 0.2413 | 0.1711 | | 0.1937 | 6.03 | 81100 | 0.2407 | 0.1689 | | 0.2106 | 6.03 | 81200 | 0.2513 | 0.1632 | | 0.1949 | 6.03 | 81300 | 0.2563 | 0.1668 | | 0.2207 | 6.03 | 81400 | 0.2649 | 0.1646 | | 0.1913 | 6.03 | 81500 | 0.2543 | 0.1682 | | 0.1991 | 6.03 | 81600 | 0.2575 | 0.1740 | | 0.1992 | 6.03 | 81700 | 0.2597 | 0.1639 | | 0.1917 | 6.03 | 81800 | 0.2571 | 0.1725 | | 0.191 | 6.03 | 81900 | 0.2595 | 0.1718 | | 0.1992 | 6.04 | 82000 | 0.2494 | 0.1697 | | 0.1839 | 6.04 | 82100 | 0.2594 | 0.1697 | | 0.1943 | 6.04 | 82200 | 0.2655 | 0.1733 | | 0.2039 | 6.04 | 82300 | 0.2690 | 0.1725 | | 0.2011 | 6.04 | 82400 | 0.2555 | 0.1711 | | 0.1964 | 6.04 | 82500 | 0.2590 | 0.1718 | | 0.1858 | 6.04 | 82600 | 0.2659 | 0.1704 | | 0.2113 | 6.04 | 82700 | 0.2534 | 0.1697 | | 0.1883 | 6.04 | 82800 | 0.2519 | 0.1711 | | 0.2005 | 6.04 | 82900 | 0.2581 | 0.1711 | | 0.2013 | 6.05 | 83000 | 0.2619 | 0.1711 | | 0.1994 | 6.05 | 83100 | 0.2566 | 0.1661 | | 0.1949 | 6.05 | 83200 | 0.2635 | 0.1711 | | 0.2002 | 6.05 | 83300 | 0.2551 | 0.1689 | | 0.1992 | 6.05 | 83400 | 0.2622 | 0.1747 | | 0.2039 | 6.05 | 83500 | 0.2567 | 0.1761 | | 0.2118 | 6.05 | 83600 | 0.2541 | 0.1711 | | 0.1999 | 6.05 | 83700 | 0.2601 | 0.1769 | | 0.1819 | 6.05 | 83800 | 0.2556 | 0.1697 | | 0.1859 | 6.05 | 83900 | 0.2523 | 0.1704 | | 0.1929 | 6.06 | 84000 | 0.2633 | 0.1747 | | 0.1854 | 6.06 | 84100 | 0.2554 | 0.1733 | | 0.2043 | 6.06 | 84200 | 0.2536 | 0.1747 | | 0.2 | 6.06 | 84300 | 0.2499 | 0.1718 | | 0.1986 | 6.06 | 84400 | 0.2446 | 0.1661 | | 0.1899 | 6.06 | 84500 | 0.2540 | 0.1689 | | 0.1881 | 6.06 | 84600 | 0.2614 | 0.1725 | | 0.2018 | 6.06 | 84700 | 0.2581 | 0.1725 | | 0.1952 | 6.06 | 84800 | 0.2632 | 0.1689 | | 0.2048 | 6.06 | 84900 | 0.2575 | 0.1689 | | 0.1951 | 6.07 | 85000 | 0.2557 | 0.1639 | | 0.1912 | 6.07 | 85100 | 0.2527 | 0.1711 | | 0.1871 | 6.07 | 85200 | 0.2535 | 0.1689 | | 0.1907 | 6.07 | 85300 | 0.2565 | 0.1682 | | 0.1899 | 6.07 | 85400 | 0.2565 | 0.1646 | | 0.1939 | 6.07 | 85500 | 0.2434 | 0.1718 | | 0.1936 | 6.07 | 85600 | 0.2602 | 0.1682 | | 0.2073 | 6.07 | 85700 | 0.2537 | 0.1682 | | 0.1944 | 6.07 | 85800 | 0.2580 | 0.1682 | | 0.1908 | 6.07 | 85900 | 0.2621 | 0.1725 | | 0.1985 | 6.08 | 86000 | 0.2652 | 0.1632 | | 0.2576 | 6.08 | 86100 | 0.1991 | 0.1725 | | 0.2994 | 6.08 | 86200 | 0.2014 | 0.1675 | | 0.29 | 6.08 | 86300 | 0.2028 | 0.1675 | | 0.2783 | 6.08 | 86400 | 0.2102 | 0.1689 | | 0.3024 | 6.08 | 86500 | 0.2031 | 0.1704 | | 0.2955 | 6.08 | 86600 | 0.2074 | 0.1661 | | 0.3126 | 6.08 | 86700 | 0.2015 | 0.1733 | | 0.2897 | 6.08 | 86800 | 0.2007 | 0.1689 | | 0.2925 | 6.08 | 86900 | 0.2058 | 0.1661 | | 0.2948 | 6.09 | 87000 | 0.2099 | 0.1697 | | 0.2827 | 6.09 | 87100 | 0.2031 | 0.1682 | | 0.3111 | 6.09 | 87200 | 0.2109 | 0.1725 | | 0.2924 | 6.09 | 87300 | 0.2021 | 0.1733 | | 0.2875 | 6.09 | 87400 | 0.2083 | 0.1718 | | 0.2672 | 6.09 | 87500 | 0.2114 | 0.1646 | | 0.279 | 6.09 | 87600 | 0.2024 | 0.1718 | | 0.2979 | 6.09 | 87700 | 0.2097 | 0.1704 | | 0.2697 | 6.09 | 87800 | 0.2103 | 0.1682 | | 0.3038 | 6.09 | 87900 | 0.2075 | 0.1646 | | 0.2784 | 6.1 | 88000 | 0.2082 | 0.1682 | | 0.2839 | 6.1 | 88100 | 0.2103 | 0.1661 | | 0.2868 | 6.1 | 88200 | 0.2059 | 0.1668 | | 0.2753 | 6.1 | 88300 | 0.2048 | 0.1682 | | 0.2866 | 6.1 | 88400 | 0.2018 | 0.1661 | | 0.3049 | 6.1 | 88500 | 0.2017 | 0.1668 | | 0.2969 | 6.1 | 88600 | 0.2037 | 0.1639 | | 0.2828 | 6.1 | 88700 | 0.2024 | 0.1675 | | 0.2888 | 6.1 | 88800 | 0.2062 | 0.1661 | | 0.2857 | 6.1 | 88900 | 0.2070 | 0.1661 | | 0.2774 | 6.11 | 89000 | 0.2028 | 0.1610 | | 0.2759 | 6.11 | 89100 | 0.2079 | 0.1646 | | 0.2809 | 6.11 | 89200 | 0.2041 | 0.1668 | | 0.2755 | 6.11 | 89300 | 0.2085 | 0.1697 | | 0.2752 | 6.11 | 89400 | 0.2063 | 0.1682 | | 0.3058 | 6.11 | 89500 | 0.2040 | 0.1689 | | 0.2948 | 6.11 | 89600 | 0.2032 | 0.1625 | | 0.2973 | 6.11 | 89700 | 0.2087 | 0.1646 | | 0.2646 | 6.11 | 89800 | 0.2074 | 0.1639 | | 0.2907 | 6.11 | 89900 | 0.2007 | 0.1610 | | 0.2919 | 6.12 | 90000 | 0.2056 | 0.1582 | | 0.2914 | 6.12 | 90100 | 0.2050 | 0.1582 | | 0.2869 | 6.12 | 90200 | 0.2040 | 0.1603 | | 0.2707 | 6.12 | 90300 | 0.2010 | 0.1632 | | 0.276 | 6.12 | 90400 | 0.2072 | 0.1668 | | 0.2919 | 6.12 | 90500 | 0.2057 | 0.1711 | | 0.2623 | 6.12 | 90600 | 0.1982 | 0.1625 | | 0.2908 | 6.12 | 90700 | 0.2046 | 0.1697 | | 0.2812 | 6.12 | 90800 | 0.2144 | 0.1625 | | 0.2753 | 6.12 | 90900 | 0.2189 | 0.1653 | | 0.2762 | 6.13 | 91000 | 0.2137 | 0.1646 | | 0.2786 | 6.13 | 91100 | 0.2124 | 0.1661 | | 0.2651 | 6.13 | 91200 | 0.2019 | 0.1646 | | 0.2688 | 6.13 | 91300 | 0.2038 | 0.1740 | | 0.2731 | 6.13 | 91400 | 0.1973 | 0.1718 | | 0.2711 | 6.13 | 91500 | 0.2022 | 0.1725 | | 0.2865 | 7.0 | 91600 | 0.1825 | 0.1718 | | 0.3023 | 7.0 | 91700 | 0.1820 | 0.1675 | | 0.2996 | 7.0 | 91800 | 0.1816 | 0.1675 | | 0.2899 | 7.0 | 91900 | 0.1808 | 0.1711 | | 0.2811 | 7.0 | 92000 | 0.1807 | 0.1668 | | 0.276 | 7.01 | 92100 | 0.1809 | 0.1675 | | 0.2987 | 7.01 | 92200 | 0.1802 | 0.1661 | | 0.2814 | 7.01 | 92300 | 0.1806 | 0.1632 | | 0.2729 | 7.01 | 92400 | 0.1802 | 0.1618 | | 0.2757 | 7.01 | 92500 | 0.1804 | 0.1618 | | 0.2843 | 7.01 | 92600 | 0.1804 | 0.1625 | | 0.253 | 7.01 | 92700 | 0.1803 | 0.1589 | | 0.2198 | 7.01 | 92800 | 0.2069 | 0.1632 | | 0.2024 | 7.01 | 92900 | 0.2118 | 0.1618 | | 0.2156 | 7.01 | 93000 | 0.2216 | 0.1639 | | 0.1975 | 7.02 | 93100 | 0.2230 | 0.1639 | | 0.1961 | 7.02 | 93200 | 0.2292 | 0.1661 | | 0.1901 | 7.02 | 93300 | 0.2307 | 0.1653 | | 0.1883 | 7.02 | 93400 | 0.2354 | 0.1646 | | 0.1884 | 7.02 | 93500 | 0.2354 | 0.1653 | | 0.2034 | 7.02 | 93600 | 0.2402 | 0.1653 | | 0.1819 | 7.02 | 93700 | 0.2362 | 0.1625 | | 0.1946 | 7.02 | 93800 | 0.2431 | 0.1639 | | 0.1965 | 7.02 | 93900 | 0.2410 | 0.1661 | | 0.2 | 7.02 | 94000 | 0.2404 | 0.1668 | | 0.1872 | 7.03 | 94100 | 0.2334 | 0.1661 | | 0.1857 | 7.03 | 94200 | 0.2357 | 0.1646 | | 0.2034 | 7.03 | 94300 | 0.2396 | 0.1625 | | 0.2067 | 7.03 | 94400 | 0.2407 | 0.1625 | | 0.2067 | 7.03 | 94500 | 0.2393 | 0.1632 | | 0.1911 | 7.03 | 94600 | 0.2402 | 0.1653 | | 0.2007 | 7.03 | 94700 | 0.2399 | 0.1675 | | 0.1903 | 7.03 | 94800 | 0.2442 | 0.1610 | | 0.1902 | 7.03 | 94900 | 0.2436 | 0.1603 | | 0.1896 | 7.03 | 95000 | 0.2479 | 0.1625 | | 0.2001 | 7.04 | 95100 | 0.2437 | 0.1632 | | 0.1845 | 7.04 | 95200 | 0.2444 | 0.1661 | | 0.1997 | 7.04 | 95300 | 0.2486 | 0.1610 | | 0.1912 | 7.04 | 95400 | 0.2467 | 0.1639 | | 0.1994 | 7.04 | 95500 | 0.2412 | 0.1618 | | 0.1902 | 7.04 | 95600 | 0.2485 | 0.1618 | | 0.1855 | 7.04 | 95700 | 0.2466 | 0.1610 | | 0.213 | 7.04 | 95800 | 0.2463 | 0.1653 | | 0.1812 | 7.04 | 95900 | 0.2481 | 0.1603 | | 0.1902 | 7.04 | 96000 | 0.2487 | 0.1589 | | 0.2014 | 7.05 | 96100 | 0.2490 | 0.1653 | | 0.1899 | 7.05 | 96200 | 0.2491 | 0.1646 | | 0.1812 | 7.05 | 96300 | 0.2524 | 0.1639 | | 0.1986 | 7.05 | 96400 | 0.2497 | 0.1632 | | 0.1995 | 7.05 | 96500 | 0.2501 | 0.1639 | | 0.2047 | 7.05 | 96600 | 0.2469 | 0.1625 | | 0.1993 | 7.05 | 96700 | 0.2471 | 0.1610 | | 0.1833 | 7.05 | 96800 | 0.2460 | 0.1618 | | 0.1892 | 7.05 | 96900 | 0.2474 | 0.1625 | | 0.1766 | 7.05 | 97000 | 0.2457 | 0.1625 | | 0.2002 | 7.06 | 97100 | 0.2484 | 0.1596 | | 0.189 | 7.06 | 97200 | 0.2457 | 0.1603 | | 0.1958 | 7.06 | 97300 | 0.2450 | 0.1610 | | 0.1962 | 7.06 | 97400 | 0.2424 | 0.1618 | | 0.201 | 7.06 | 97500 | 0.2400 | 0.1632 | | 0.1915 | 7.06 | 97600 | 0.2421 | 0.1639 | | 0.1887 | 7.06 | 97700 | 0.2417 | 0.1639 | | 0.2027 | 7.06 | 97800 | 0.2422 | 0.1646 | | 0.192 | 7.06 | 97900 | 0.2447 | 0.1618 | | 0.199 | 7.06 | 98000 | 0.2439 | 0.1618 | | 0.1905 | 7.07 | 98100 | 0.2428 | 0.1618 | | 0.1914 | 7.07 | 98200 | 0.2424 | 0.1618 | | 0.1819 | 7.07 | 98300 | 0.2426 | 0.1610 | | 0.1927 | 7.07 | 98400 | 0.2441 | 0.1603 | | 0.194 | 7.07 | 98500 | 0.2454 | 0.1610 | | 0.2013 | 7.07 | 98600 | 0.2429 | 0.1603 | | 0.1904 | 7.07 | 98700 | 0.2442 | 0.1603 | | 0.1915 | 7.07 | 98800 | 0.2443 | 0.1603 | | 0.1809 | 7.07 | 98900 | 0.2438 | 0.1610 | | 0.1977 | 7.07 | 99000 | 0.2447 | 0.1596 | | 0.1893 | 7.08 | 99100 | 0.2462 | 0.1596 | | 0.3181 | 7.08 | 99200 | 0.1927 | 0.1639 | | 0.3076 | 7.08 | 99300 | 0.1867 | 0.1661 | | 0.2971 | 7.08 | 99400 | 0.1874 | 0.1639 | | 0.2919 | 7.08 | 99500 | 0.1879 | 0.1668 | | 0.3001 | 7.08 | 99600 | 0.1885 | 0.1646 | | 0.2837 | 7.08 | 99700 | 0.1887 | 0.1639 | | 0.3188 | 7.08 | 99800 | 0.1885 | 0.1632 | | 0.3076 | 7.08 | 99900 | 0.1884 | 0.1625 | | 0.272 | 7.08 | 100000 | 0.1884 | 0.1618 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

madhaviit/llama-2-7b-madhav-t1-v2

madhaviit

transformers

text-generation

Entry not found

ducha07/ASR-test

ducha07

transformers

automatic-speech-recognition

--- language: - vi license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer datasets: - ducha07/audio_HTV_thoisu metrics: - wer model-index: - name: ASR-test results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: HTV news type: ducha07/audio_HTV_thoisu metrics: - name: Wer type: wer value: 0.2796665364074508 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ASR-test-1 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the HTV news dataset. It achieves the following results on the evaluation set: - Loss: 0.6593 - Wer: 0.2797 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.8562 | 0.92 | 100 | 0.8316 | 0.4500 | | 1.0777 | 1.83 | 200 | 0.6898 | 0.3899 | | 0.98 | 2.75 | 300 | 0.6811 | 0.3740 | | 0.8967 | 3.67 | 400 | 0.6332 | 0.3565 | | 0.8965 | 4.59 | 500 | 0.6038 | 0.3517 | | 0.8396 | 5.5 | 600 | 0.6040 | 0.3479 | | 0.8137 | 6.42 | 700 | 0.5929 | 0.3408 | | 0.8304 | 7.34 | 800 | 0.5911 | 0.3513 | | 0.7894 | 8.26 | 900 | 0.6078 | 0.3357 | | 0.7412 | 9.17 | 1000 | 0.6214 | 0.3230 | | 0.7653 | 10.09 | 1100 | 0.5869 | 0.3444 | | 0.7437 | 11.01 | 1200 | 0.5906 | 0.3213 | | 0.7083 | 11.93 | 1300 | 0.5952 | 0.3139 | | 0.7168 | 12.84 | 1400 | 0.5721 | 0.3267 | | 0.7008 | 13.76 | 1500 | 0.5895 | 0.3177 | | 0.6825 | 14.68 | 1600 | 0.5909 | 0.3098 | | 0.6989 | 15.6 | 1700 | 0.5979 | 0.3673 | | 0.6717 | 16.51 | 1800 | 0.5863 | 0.3077 | | 0.6496 | 17.43 | 1900 | 0.5798 | 0.3043 | | 0.6609 | 18.35 | 2000 | 0.5787 | 0.3555 | | 0.628 | 19.27 | 2100 | 0.5889 | 0.3133 | | 0.6322 | 20.18 | 2200 | 0.5913 | 0.3077 | | 0.634 | 21.1 | 2300 | 0.5769 | 0.3193 | | 0.6172 | 22.02 | 2400 | 0.5731 | 0.3005 | | 0.6043 | 22.94 | 2500 | 0.5820 | 0.3075 | | 0.6051 | 23.85 | 2600 | 0.5831 | 0.3435 | | 0.5865 | 24.77 | 2700 | 0.5790 | 0.3029 | | 0.5806 | 25.69 | 2800 | 0.5945 | 0.3053 | | 0.5901 | 26.61 | 2900 | 0.5780 | 0.3126 | | 0.5769 | 27.52 | 3000 | 0.5732 | 0.2963 | | 0.5539 | 28.44 | 3100 | 0.5837 | 0.2950 | | 0.5799 | 29.36 | 3200 | 0.5835 | 0.3178 | | 0.5518 | 30.28 | 3300 | 0.5941 | 0.2943 | | 0.549 | 31.19 | 3400 | 0.5960 | 0.2979 | | 0.5612 | 32.11 | 3500 | 0.5747 | 0.3167 | | 0.5411 | 33.03 | 3600 | 0.5855 | 0.2978 | | 0.536 | 33.94 | 3700 | 0.5720 | 0.2944 | | 0.5329 | 34.86 | 3800 | 0.5998 | 0.3186 | | 0.5185 | 35.78 | 3900 | 0.5936 | 0.2884 | | 0.5186 | 36.7 | 4000 | 0.5773 | 0.2901 | | 0.5027 | 37.61 | 4100 | 0.5969 | 0.3264 | | 0.52 | 38.53 | 4200 | 0.6184 | 0.2939 | | 0.4992 | 39.45 | 4300 | 0.5887 | 0.2943 | | 0.5064 | 40.37 | 4400 | 0.5814 | 0.2966 | | 0.4928 | 41.28 | 4500 | 0.6128 | 0.2902 | | 0.508 | 42.2 | 4600 | 0.5943 | 0.2923 | | 0.4887 | 43.12 | 4700 | 0.6100 | 0.3039 | | 0.4872 | 44.04 | 4800 | 0.6044 | 0.2875 | | 0.4711 | 44.95 | 4900 | 0.5961 | 0.2974 | | 0.4813 | 45.87 | 5000 | 0.6022 | 0.2945 | | 0.4818 | 46.79 | 5100 | 0.6199 | 0.2898 | | 0.4492 | 47.71 | 5200 | 0.6161 | 0.2943 | | 0.4715 | 48.62 | 5300 | 0.6038 | 0.2838 | | 0.4601 | 49.54 | 5400 | 0.6223 | 0.2829 | | 0.4432 | 50.46 | 5500 | 0.6058 | 0.2965 | | 0.4419 | 51.38 | 5600 | 0.6134 | 0.2917 | | 0.4564 | 52.29 | 5700 | 0.6124 | 0.2857 | | 0.4349 | 53.21 | 5800 | 0.6229 | 0.2877 | | 0.4358 | 54.13 | 5900 | 0.6095 | 0.2898 | | 0.4432 | 55.05 | 6000 | 0.6365 | 0.2881 | | 0.4277 | 55.96 | 6100 | 0.6169 | 0.2870 | | 0.4397 | 56.88 | 6200 | 0.6174 | 0.2849 | | 0.4245 | 57.8 | 6300 | 0.6340 | 0.2858 | | 0.4203 | 58.72 | 6400 | 0.6321 | 0.2909 | | 0.4112 | 59.63 | 6500 | 0.6243 | 0.2866 | | 0.4244 | 60.55 | 6600 | 0.6318 | 0.2775 | | 0.4119 | 61.47 | 6700 | 0.6215 | 0.2798 | | 0.403 | 62.39 | 6800 | 0.6213 | 0.2829 | | 0.4158 | 63.3 | 6900 | 0.6451 | 0.2795 | | 0.3997 | 64.22 | 7000 | 0.6317 | 0.2854 | | 0.4006 | 65.14 | 7100 | 0.6329 | 0.2846 | | 0.4051 | 66.06 | 7200 | 0.6318 | 0.2834 | | 0.3953 | 66.97 | 7300 | 0.6442 | 0.2855 | | 0.4119 | 67.89 | 7400 | 0.6345 | 0.2893 | | 0.3976 | 68.81 | 7500 | 0.6361 | 0.2798 | | 0.3965 | 69.72 | 7600 | 0.6355 | 0.2853 | | 0.3957 | 70.64 | 7700 | 0.6457 | 0.2814 | | 0.3837 | 71.56 | 7800 | 0.6396 | 0.2855 | | 0.3893 | 72.48 | 7900 | 0.6424 | 0.2842 | | 0.3816 | 73.39 | 8000 | 0.6496 | 0.2778 | | 0.3855 | 74.31 | 8100 | 0.6427 | 0.2881 | | 0.3767 | 75.23 | 8200 | 0.6394 | 0.2858 | | 0.3747 | 76.15 | 8300 | 0.6513 | 0.2844 | | 0.3829 | 77.06 | 8400 | 0.6602 | 0.2775 | | 0.3721 | 77.98 | 8500 | 0.6427 | 0.2825 | | 0.3708 | 78.9 | 8600 | 0.6507 | 0.2847 | | 0.3767 | 79.82 | 8700 | 0.6518 | 0.2816 | | 0.3655 | 80.73 | 8800 | 0.6597 | 0.2802 | | 0.3614 | 81.65 | 8900 | 0.6542 | 0.2781 | | 0.3629 | 82.57 | 9000 | 0.6520 | 0.2782 | | 0.3621 | 83.49 | 9100 | 0.6501 | 0.2797 | | 0.3616 | 84.4 | 9200 | 0.6528 | 0.2777 | | 0.3519 | 85.32 | 9300 | 0.6549 | 0.2798 | | 0.3572 | 86.24 | 9400 | 0.6541 | 0.2789 | | 0.3585 | 87.16 | 9500 | 0.6497 | 0.2778 | | 0.3531 | 88.07 | 9600 | 0.6523 | 0.2781 | | 0.3586 | 88.99 | 9700 | 0.6578 | 0.2789 | | 0.3463 | 89.91 | 9800 | 0.6565 | 0.2816 | | 0.3508 | 90.83 | 9900 | 0.6559 | 0.2797 | | 0.3513 | 91.74 | 10000 | 0.6611 | 0.2794 | | 0.3425 | 92.66 | 10100 | 0.6538 | 0.2804 | | 0.3596 | 93.58 | 10200 | 0.6639 | 0.2808 | | 0.3632 | 94.5 | 10300 | 0.6561 | 0.2789 | | 0.348 | 95.41 | 10400 | 0.6556 | 0.2786 | | 0.3514 | 96.33 | 10500 | 0.6575 | 0.2791 | | 0.3499 | 97.25 | 10600 | 0.6573 | 0.2795 | | 0.3353 | 98.17 | 10700 | 0.6589 | 0.2797 | | 0.3468 | 99.08 | 10800 | 0.6589 | 0.2799 | | 0.3571 | 100.0 | 10900 | 0.6593 | 0.2797 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

hkivancoral/smids_3x_beit_base_sgd_0001_fold5

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_sgd_0001_fold5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.7833333333333333 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_sgd_0001_fold5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5470 - Accuracy: 0.7833 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.227 | 1.0 | 225 | 1.2590 | 0.3417 | | 1.1831 | 2.0 | 450 | 1.2003 | 0.3717 | | 1.084 | 3.0 | 675 | 1.1460 | 0.4017 | | 1.0157 | 4.0 | 900 | 1.0941 | 0.42 | | 0.9735 | 5.0 | 1125 | 1.0438 | 0.455 | | 0.9436 | 6.0 | 1350 | 0.9975 | 0.4917 | | 0.9179 | 7.0 | 1575 | 0.9541 | 0.5333 | | 0.8784 | 8.0 | 1800 | 0.9154 | 0.5717 | | 0.884 | 9.0 | 2025 | 0.8784 | 0.595 | | 0.8367 | 10.0 | 2250 | 0.8446 | 0.6217 | | 0.8299 | 11.0 | 2475 | 0.8151 | 0.6483 | | 0.7845 | 12.0 | 2700 | 0.7879 | 0.6617 | | 0.7765 | 13.0 | 2925 | 0.7642 | 0.6667 | | 0.7469 | 14.0 | 3150 | 0.7428 | 0.6783 | | 0.7257 | 15.0 | 3375 | 0.7247 | 0.69 | | 0.6997 | 16.0 | 3600 | 0.7073 | 0.7 | | 0.7213 | 17.0 | 3825 | 0.6920 | 0.7083 | | 0.698 | 18.0 | 4050 | 0.6780 | 0.7183 | | 0.7064 | 19.0 | 4275 | 0.6649 | 0.7217 | | 0.6988 | 20.0 | 4500 | 0.6533 | 0.735 | | 0.6396 | 21.0 | 4725 | 0.6426 | 0.7383 | | 0.6558 | 22.0 | 4950 | 0.6328 | 0.7483 | | 0.6628 | 23.0 | 5175 | 0.6239 | 0.75 | | 0.6417 | 24.0 | 5400 | 0.6165 | 0.7533 | | 0.6414 | 25.0 | 5625 | 0.6079 | 0.7517 | | 0.6773 | 26.0 | 5850 | 0.6018 | 0.7567 | | 0.662 | 27.0 | 6075 | 0.5968 | 0.7583 | | 0.6119 | 28.0 | 6300 | 0.5913 | 0.765 | | 0.6058 | 29.0 | 6525 | 0.5864 | 0.765 | | 0.5469 | 30.0 | 6750 | 0.5816 | 0.7683 | | 0.6085 | 31.0 | 6975 | 0.5777 | 0.7667 | | 0.557 | 32.0 | 7200 | 0.5744 | 0.7667 | | 0.5975 | 33.0 | 7425 | 0.5708 | 0.7683 | | 0.5747 | 34.0 | 7650 | 0.5675 | 0.7717 | | 0.6075 | 35.0 | 7875 | 0.5645 | 0.7717 | | 0.5661 | 36.0 | 8100 | 0.5618 | 0.7733 | | 0.5862 | 37.0 | 8325 | 0.5597 | 0.7733 | | 0.5867 | 38.0 | 8550 | 0.5581 | 0.775 | | 0.5414 | 39.0 | 8775 | 0.5562 | 0.7767 | | 0.5431 | 40.0 | 9000 | 0.5546 | 0.775 | | 0.5693 | 41.0 | 9225 | 0.5532 | 0.7767 | | 0.5499 | 42.0 | 9450 | 0.5518 | 0.7783 | | 0.5959 | 43.0 | 9675 | 0.5505 | 0.78 | | 0.6402 | 44.0 | 9900 | 0.5495 | 0.78 | | 0.5702 | 45.0 | 10125 | 0.5486 | 0.7817 | | 0.5765 | 46.0 | 10350 | 0.5481 | 0.7833 | | 0.6208 | 47.0 | 10575 | 0.5477 | 0.7833 | | 0.5613 | 48.0 | 10800 | 0.5473 | 0.7833 | | 0.6326 | 49.0 | 11025 | 0.5471 | 0.7833 | | 0.5777 | 50.0 | 11250 | 0.5470 | 0.7833 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

yutingg/essay_clarity

yutingg

Entry not found

hkivancoral/smids_3x_beit_base_rms_001_fold5

hkivancoral

transformers

image-classification

--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: smids_3x_beit_base_rms_001_fold5 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.8216666666666667 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smids_3x_beit_base_rms_001_fold5 This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1444 - Accuracy: 0.8217 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.9503 | 1.0 | 225 | 0.9116 | 0.5117 | | 0.9706 | 2.0 | 450 | 0.9826 | 0.46 | | 0.8 | 3.0 | 675 | 0.8216 | 0.55 | | 0.7869 | 4.0 | 900 | 0.7274 | 0.6417 | | 0.7386 | 5.0 | 1125 | 0.7210 | 0.65 | | 0.6956 | 6.0 | 1350 | 0.8161 | 0.6183 | | 0.8586 | 7.0 | 1575 | 0.7427 | 0.6283 | | 0.6974 | 8.0 | 1800 | 0.7391 | 0.6467 | | 0.6497 | 9.0 | 2025 | 0.6781 | 0.665 | | 0.665 | 10.0 | 2250 | 0.6784 | 0.69 | | 0.6749 | 11.0 | 2475 | 0.6355 | 0.7083 | | 0.6727 | 12.0 | 2700 | 0.6116 | 0.7083 | | 0.6759 | 13.0 | 2925 | 0.6229 | 0.715 | | 0.6034 | 14.0 | 3150 | 0.6562 | 0.685 | | 0.5372 | 15.0 | 3375 | 0.5788 | 0.755 | | 0.539 | 16.0 | 3600 | 0.5524 | 0.7583 | | 0.5144 | 17.0 | 3825 | 0.5824 | 0.7483 | | 0.4796 | 18.0 | 4050 | 0.5455 | 0.7617 | | 0.5096 | 19.0 | 4275 | 0.5692 | 0.765 | | 0.4664 | 20.0 | 4500 | 0.5893 | 0.7533 | | 0.3623 | 21.0 | 4725 | 0.5578 | 0.745 | | 0.3075 | 22.0 | 4950 | 0.5688 | 0.7867 | | 0.3806 | 23.0 | 5175 | 0.5983 | 0.7633 | | 0.4403 | 24.0 | 5400 | 0.4856 | 0.8017 | | 0.3263 | 25.0 | 5625 | 0.4951 | 0.8083 | | 0.4298 | 26.0 | 5850 | 0.5186 | 0.8067 | | 0.3696 | 27.0 | 6075 | 0.5017 | 0.8017 | | 0.3505 | 28.0 | 6300 | 0.5055 | 0.805 | | 0.2809 | 29.0 | 6525 | 0.5401 | 0.81 | | 0.2639 | 30.0 | 6750 | 0.5378 | 0.8083 | | 0.1827 | 31.0 | 6975 | 0.5714 | 0.815 | | 0.2309 | 32.0 | 7200 | 0.5483 | 0.8167 | | 0.2167 | 33.0 | 7425 | 0.5706 | 0.7967 | | 0.1201 | 34.0 | 7650 | 0.6703 | 0.8117 | | 0.1274 | 35.0 | 7875 | 0.7662 | 0.7917 | | 0.1115 | 36.0 | 8100 | 0.6767 | 0.8183 | | 0.1604 | 37.0 | 8325 | 0.8509 | 0.8083 | | 0.0668 | 38.0 | 8550 | 0.7497 | 0.8233 | | 0.1178 | 39.0 | 8775 | 0.8497 | 0.8067 | | 0.0788 | 40.0 | 9000 | 0.9494 | 0.8033 | | 0.0775 | 41.0 | 9225 | 0.9252 | 0.81 | | 0.1033 | 42.0 | 9450 | 0.9696 | 0.8217 | | 0.0903 | 43.0 | 9675 | 0.9856 | 0.8133 | | 0.037 | 44.0 | 9900 | 1.0200 | 0.81 | | 0.019 | 45.0 | 10125 | 1.1824 | 0.8067 | | 0.0484 | 46.0 | 10350 | 1.0838 | 0.8183 | | 0.0259 | 47.0 | 10575 | 1.1218 | 0.8083 | | 0.0077 | 48.0 | 10800 | 1.1617 | 0.8133 | | 0.0106 | 49.0 | 11025 | 1.1590 | 0.8117 | | 0.0158 | 50.0 | 11250 | 1.1444 | 0.8217 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2

linqus/bert-finetuned-ner

linqus

transformers

token-classification

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9328715157512782 - name: Recall type: recall value: 0.9518680578929654 - name: F1 type: f1 value: 0.9422740524781341 - name: Accuracy type: accuracy value: 0.9866515570730559 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0670 - Precision: 0.9329 - Recall: 0.9519 - F1: 0.9423 - Accuracy: 0.9867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.025 | 1.0 | 1756 | 0.0677 | 0.9269 | 0.9472 | 0.9369 | 0.9848 | | 0.0227 | 2.0 | 3512 | 0.0681 | 0.9302 | 0.9482 | 0.9391 | 0.9857 | | 0.015 | 3.0 | 5268 | 0.0670 | 0.9329 | 0.9519 | 0.9423 | 0.9867 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

magnifi/llama-2-classifier-v3

magnifi

transformers

text-generation

Entry not found

ManthanKulakarni/phi_finetuned_v1

ManthanKulakarni

Entry not found

magnifi/zephyr-classifier-v3-all

magnifi

transformers

text-generation

Entry not found

SheepVipPro/Sheep

SheepVipPro

--- license: other license_name: sheep license_link: LICENSE ---