Reference documentation and code samples for the Google Cloud Dialogflow V2 Client class InferenceParameter.
The parameters of inference.
Generated from protobuf message google.cloud.dialogflow.v2.InferenceParameter
Namespace
Google \ Cloud \ Dialogflow \ V2Methods
__construct
Constructor.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ max_output_tokens |
int
Optional. Maximum number of the output tokens for the generator. |
↳ temperature |
float
Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0. |
↳ top_k |
int
Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40. |
↳ top_p |
float
Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95. |
getMaxOutputTokens
Optional. Maximum number of the output tokens for the generator.
| Returns | |
|---|---|
| Type | Description |
int |
|
hasMaxOutputTokens
clearMaxOutputTokens
setMaxOutputTokens
Optional. Maximum number of the output tokens for the generator.
| Parameter | |
|---|---|
| Name | Description |
var |
int
|
| Returns | |
|---|---|
| Type | Description |
$this |
|
getTemperature
Optional. Controls the randomness of LLM predictions.
Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.
| Returns | |
|---|---|
| Type | Description |
float |
|
hasTemperature
clearTemperature
setTemperature
Optional. Controls the randomness of LLM predictions.
Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.
| Parameter | |
|---|---|
| Name | Description |
var |
float
|
| Returns | |
|---|---|
| Type | Description |
$this |
|
getTopK
Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling.
Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.
| Returns | |
|---|---|
| Type | Description |
int |
|
hasTopK
clearTopK
setTopK
Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling.
Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.
| Parameter | |
|---|---|
| Name | Description |
var |
int
|
| Returns | |
|---|---|
| Type | Description |
$this |
|
getTopP
Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95.
Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.
| Returns | |
|---|---|
| Type | Description |
float |
|
hasTopP
clearTopP
setTopP
Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95.
Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.
| Parameter | |
|---|---|
| Name | Description |
var |
float
|
| Returns | |
|---|---|
| Type | Description |
$this |
|