Documentation fixes

docs/scripts/build_readme.py

@@ -69,6 +69,8 @@
 CUSTOM_LINK_MAP = {
     '/custom_usage#convert-your-models-to-onnx': '#convert-your-models-to-onnx',
     './api/env': DOCS_BASE_URL + '/api/env',
+    './guides/webgpu': DOCS_BASE_URL + '/guides/webgpu',
+    './guides/dtypes': DOCS_BASE_URL + '/guides/dtypes',
 }
 
 

docs/snippets/1_quick-tour.snippet

@@ -52,7 +52,7 @@ const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncase
 });
 ```
 
-For more information, check out the [WebGPU guide](/guides/webgpu).
+For more information, check out the [WebGPU guide](./guides/webgpu).
 
 > [!WARNING]
 > The WebGPU API is still experimental in many browsers, so if you run into any issues,
@@ -62,7 +62,7 @@ In resource-constrained environments, such as web browsers, it is advisable to u
 the model to lower bandwidth and optimize performance. This can be achieved by adjusting the `dtype` option,
 which allows you to select the appropriate data type for your model. While the available options may vary
 depending on the specific model, typical choices include `"fp32"` (default for WebGPU), `"fp16"`, `"q8"`
-(default for WASM), and `"q4"`. For more information, check out the [quantization guide](../guides/dtypes).
+(default for WASM), and `"q4"`. For more information, check out the [quantization guide](./guides/dtypes).
 ```javascript
 // Run the model at 4-bit quantization
 const pipe = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english', {

docs/snippets/5_supported-tasks.snippet

@@ -7,7 +7,7 @@
 |--------------------------|----|-------------|------------|
 | [Fill-Mask](https://huggingface.co/tasks/fill-mask)                     | `fill-mask`   | Masking some of the words in a sentence and predicting which words should replace those masks. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FillMaskPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=fill-mask&library=transformers.js) |
 | [Question Answering](https://huggingface.co/tasks/question-answering)   | `question-answering`   | Retrieve the answer to a question from a given text. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.QuestionAnsweringPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=question-answering&library=transformers.js) |
-| [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | `sentence-similarity`  | Determining how similar two texts are. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=feature-extraction&library=transformers.js) |
+| [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | `sentence-similarity`  | Determining how similar two texts are. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=sentence-similarity&library=transformers.js) |
 | [Summarization](https://huggingface.co/tasks/summarization)             |  `summarization`  | Producing a shorter version of a document while preserving its important information. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.SummarizationPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=summarization&library=transformers.js) |
 | [Table Question Answering](https://huggingface.co/tasks/table-question-answering) |  `table-question-answering`  | Answering a question about information from a given table. | ❌ |
 | [Text Classification](https://huggingface.co/tasks/text-classification)      | `text-classification` or `sentiment-analysis`  | Assigning a label or class to a given text. | ✅ [(docs)](https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.TextClassificationPipeline)<br>[(models)](https://huggingface.co/models?pipeline_tag=text-classification&library=transformers.js) |