Evaluations
Run models against your data
Introducing Evaluations, a powerful feature designed to enable you to effortlessly test and compare a selection of AI models against your datasets.
Whether you're fine-tuning models or evaluating performance metrics, Oxen evaluations simplifies the process, allowing you to quickly and easily run prompts through an entire dataset.
Once you're happy with the results, output the resulting dataset to a new file, another branch, or directly as a new commit.
87897a5f-ce8f-4c2d-81c5-587bb0182181
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
1 month ago
Classify the text into positive, negative or neutral. all lowercase

{text}
completed 5 row sample242 tokens$ 0.0007 2 iterations
c02f2ed2-101e-4c96-b151-d8ea3b83b156
OpenAIOpenAI/GPT 4o minitext → text
Eloy
2 months ago
Describe the sentimen of this text. it can be either positive or negative {text}
completed 5 row sample447 tokens$ 0.0002 1 iteration
62e264e3-afd9-4366-a3ae-ce87dcf52c19
OpenAIOpenAI/o1 minitext → text
Bessie
ox
5 months ago
What is the sentiment of the following text, limit to one word, positive, negative or neutral

{text}
completed 5 row sample1550 tokens$ 0.0151 2 iterations
7c2aa00c-b9d8-4e38-918d-a6e9ec0960ba
MetaMeta/Llama 3.1 70B Instructtext → text
Bessie
ox
5 months ago
Classify the following text into positive, negative or neutral, based on the sentiment of the text. All lowercase, one word.

{text}
evaluating_sentiment_llama_70B
completed 4000 rows286738 tokens$ 0.2581 3 iterations
27fb870b-9302-4616-af19-3590a7aff15f
GoogleGoogle/Gemini 1.5 Flash - 8Btext → text
Bessie
ox
5 months ago
Classify the text into positive, negative or neutral sentiment. One word all lowercase.

{text}
completed 5 row sample266 tokens$ 0.0000 2 iterations
57993a40-2bb5-46a6-b9e6-a619d457132c
Mistral AIMistral AI/Mistral Large 2text → text
Bessie
ox
5 months ago
Compute the sentiment of the text based on how well the companies are performing in the market.
Return only one of three options: positive, negative, or neutral.
Respond with one word, all lowercase.

Text:
{text}
sentiment-analysis-mistral-large
sentiment-analysis-mistral-large-take-2
completed 1000 rows54065 tokens$ 0.1106 2 iterations
fb24aca8-7598-4208-bd15-3dd8630285f7
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
5 months ago
Compute the sentiment of the text based on how well the companies are performing in the market.
Return only one of three options: positive, negative, or neutral.
Respond with one word, all lowercase.

Text:
{text}
sentiment-analysis-gpt-4o
completed 1000 rows77875 tokens$ 0.2022 1 iteration
fc6c8632-4ef2-4b80-9c7f-8753b7921f5b
Mistral AIMistral AI/Mistral Large 2text → text
Bessie
ox
5 months ago
Compute the sentiment of the text based on how well the companies are performing in the market.
Return only one of three options: positive, negative, or neutral.
Respond with one word, all lowercase.

Text:
{text}
sentiment-analysis-mistral-large
completed 1000 rows50440 tokens$ 0.1032 2 iterations
889f014a-d37e-4ab7-969a-f31ab77f9c58
Mistral AIMistral AI/Ministral 8Btext → text
Bessie
ox
5 months ago
Compute the sentiment of the text based on how well the companies are performing in the market.
Return only one of three options: positive, negative, or neutral.
Respond with one word, all lowercase.

Text:
{text}
sentiment-analysis-ministral-8b
completed 1000 rows38411 tokens$ 0.0038 2 iterations
0c7bfd16-c0c2-4327-b915-2a9e8e7cc2b7
Mistral AIMistral AI/Ministral 3Btext → text
Bessie
ox
5 months ago
Compute the sentiment of the text based on how well the companies are performing in the market.
Return only one of three options: positive, negative, or neutral.
Respond with one word, all lowercase.

Text:
{text}
sentiment-analysis-ministral-3b
completed 1000 rows38280 tokens$ 0.0015 2 iterations
c8d6b4ac-ae59-4001-b4a4-920e82c64b78
OpenAIOpenAI/GPT 4o minitext → text
Bessie
ox
5 months ago
Fix the punctuation in the following text

{Sentence}
completed 842 rows58439 tokens$ 0.0192 1 iteration
7a82f117-0432-4b39-981b-6bcb152c91e9
OpenAIOpenAI/GPT 4o minitext → text
Bessie
ox
5 months ago
Fix the punctuation in the following text

{Sentence}
completed 4000 rows283425 tokens$ 0.0933 2 iterations
e98a97a2-ddbc-4e2c-9aea-c33bcbe509f3
OpenAIOpenAI/GPT 4o minitext → text
Bessie
ox
5 months ago
Fix the punctuation in the following text

{text}
completed 1000 rows70157 tokens$ 0.0231 2 iterations
d6795b42-8c63-42d6-a140-1e51a4da7f3f
OpenAIOpenAI/GPT 4o minitext → text
Bessie
ox
5 months ago
What is the sentiment of the following text. Please respond with positive, negative or neutral. All one word. All lowercase.

{text}
completed 1999 rows122466 tokens$ 0.0193 3 iterations
cbd282ff-723b-474c-bfbb-6b7caaadec94
OpenAIOpenAI/GPT 4o minitext → text
Bessie
ox
5 months ago
Compute sentiment for the following text. One word, all lowercase. Positive, negative or neutral

{text}
completed 5 row sample256 tokens$ 0.0000 2 iterations
e5b81e95-a028-43c7-b3c4-b34cb96fcd04
MetaMeta/Llama 3.1 8B Instruct Turbotext → text
Bessie
ox
6 months ago
You are a financial analyst and you want to find if the companies mentioned are mentioned in a positive, negative or neutral light. Respond all lowercase, one word.

{text}
completed 100 rows10262 tokens$ 0.0018 3 iterations
e9ba9a18-c973-450c-9514-049e85f3f20d
MetaMeta/Llama 3.1 8B Instruct Turbotext → text
Bessie
ox
6 months ago
Compute the sentiment of the text based on the how well the companies are performing in the market. 

Return only one of three options: positive, negative or neutral. 

Respond with one word all lowercase.

Text:
{text}
llama-3.1-8b-sentiment
completed 100 rows11104 tokens$ 0.0020 2 iterations
abd94a83-3768-4115-bd51-22adbf8266de
Mistral AIMistral AI/Ministral 3Btext → text
Bessie
ox
6 months ago
compute the sentiment of the text, positive, negative or neutral, one word all lowercase

{text}
ministral-3b-sentiment
completed 100 rows5293 tokens$ 0.0002 2 iterations
e0bbf280-af43-4d71-a5d2-a2a834f93cae
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
compute the sentiment of the text, positive, negative or neutral, one word all lowercase

{text}
completed 5 row sample291 tokens 2 iterations
850b4c99-ac01-4210-81f6-5dda2ba3285a
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Classify the text into positive negave or neutral sentiment. respond with one word all lowercase.

{text}
completed 100 rows5654 tokens 3 iterations
fcb0b14c-5565-4c4b-8ce3-8726ad23174e
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Find the sentiment of the following text, respond with just one word: positive, negative or neutral all lowercase

{text}
completed 5 row sample311 tokens 3 iterations
34042ba6-86bb-48eb-89c2-f00b7afcf401
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Classify the text into positive, negative or neutral sentiment

{text}
completed 5 row sample228 tokens 1 iteration
79aa0000-3297-4ac8-9866-7d20c0ff203c
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Translate this from English to French

{text}
completed 5 row sample358 tokens 1 iteration
c27f903b-5b0e-4be8-afc9-0522997cdee0
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Translate this from English to French

{text}
completed 5 row sample357 tokens 1 iteration
c880bd08-4b52-4c9b-9c06-39700d140d5f
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Classify the sentiment, respond in one word

{text}
completed 5 row sample210 tokens 1 iteration
15e20ce8-ca37-4786-b13c-b42b350ee1e5
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Classify this into positive, negative, neutral, respond with only one word.

{text}
completed 100 rows5289 tokens 3 iterations
d97a086a-d502-40d4-9413-7a62da39ee95
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Classify this into positive, negative, or neutral. Respond with one word, all lowercase.

{text}
completed 10 row sample585 tokens 1 iteration
78f86efd-7bc7-44d8-a06a-29a8470c29e7
OpenAIOpenAI/GPT 4otext → text
artforge
6 months ago
You are a financial analyst who sees bad market outcomes as good and good market outcomes as bad because you are trying to short companies. Answer with a number between -10 and 10 with 10 meaning extremely positive opportunity to short, and -10 meaning an extremely negative opportinity to short response ONLY with the number

{text}
completed 10 row sample1019 tokens 7 iterations
58126199-3ec5-42a2-935a-9312f4d892f3
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
Text: Operating profit improved by 39.9% to EUR 18.0 mn from EUR12.8 mn.
Sentiment: negative

Text: Net sales have been eaten by the weak US dollar.
Sentiment: positive

Text: Includes company and brand share data by category, as well as distribution channel data.
Sentiment: neutral

Text: {text}
Sentiment: 
completed 100 rows11470 tokens 3 iterations
9cc8fbb8-1087-4eac-be53-f79269984524
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
classify the text into positive, negative, or neutral sentiment. Respond with a single word, all lowercase. Think really hard about if it is positive or negative. My career depends on you getting it right!

{text}
completed 100 rows7954 tokens 2 iterations
e1a47544-2405-4e9a-b02e-5e3a65b943b9
OpenAIOpenAI/GPT 4otext → text
Bessie
ox
6 months ago
classify the text into positive, negative, or neutral sentiment. Respond with a single word, all lowercase.

{text}
style_prompting
completed 100 rows5954 tokens 2 iterations